Lecture 5: Analyzing Massive Graphs - Part II

The Art of Analyzing Big Data - The Data Scientist’s Toolbox

0. Package Setup

For this lecture, we are going to use the Kaggle, TuriCreate, and Networkx packages. Let's set them up:

In [1]:
# Installing the Kaggle package
!pip install kaggle 

#Important Note: complete this with your own key - after running this for the first time remmember to **remove** your API_KEY
api_token = {"username":"<Insert Your Kaggle User Name>","key":"<Insert Your Kaggle API key>"}

# creating kaggle.json file with the personal API-Key details 
# You can also put this file on your Google Drive
with open('~/.kaggle/kaggle.json', 'w') as file:
  json.dump(api_token, file)
!chmod 600 ~/.kaggle/kaggle.json
In [2]:
!pip install turicreate
!pip install networkx
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Creating a Simple Friends Recommender

We can use networks to give recommendations to a vertex about other vertices it may want to connect to (with a link). One straightforward example to achieve this is by finding friends-of-friends with the highest number of common friends. For example, if we look at the Marvel Superheroes graph and want to give Antman suggestions about other heroes/villains he may want to interact with, we can look at the heroes Antman already connects to in the network. Then, we look at the heroes connected to the heroes that are already connected to Antman, i.e., friends-of-friends. Next, we can rank these friends-of-friends according to the number of common connections they have with Antman, i.e., according to their common-friends number with Antman.

Let's use Networkx to create a simple recommendation algorithm. First, let load the Marvel Universe Social Network dataset into a Networkx object:

In [ ]:
# Installing the Kaggle package
!pip install kaggle 

#Important Note: complete this with your own key - after running this for the first time remmember to **remove** your API_KEY
api_token = {"username":"<Insert Your Kaggle User Name>","key":"<Insert Your Kaggle API key>"}

# creating kaggle.json file with the personal API-Key details 
# You can also put this file on your Google Drive
with open('~/.kaggle/kaggle.json', 'w') as file:
  json.dump(api_token, file)
!chmod 600 ~/.kaggle/kaggle.json
In [3]:
import networkx as nx
import turicreate as tc 


e_sf = tc.SFrame.read_csv("./datasets/the-marvel-universe-social-network/hero-network.csv")
g = nx.Graph() # Creating Undirected Graph
# adding all nodes and vertices at once
g.add_edges_from([(r['hero1'],r['hero2']) for r in e_sf])
print(nx.info(g))
Finished parsing file /Users/michael/Dropbox (BGU)/massive data mining/ 2020/notebooks/datasets/the-marvel-universe-social-network/hero-network.csv
Parsing completed. Parsed 100 lines in 0.397145 secs.
------------------------------------------------------
Inferred types from first 100 line(s) of file as 
column_type_hints=[str,str]
If parsing fails due to incorrect types, you can correct
the inferred type list above and pass it to read_csv in
the column_type_hints argument
------------------------------------------------------
Finished parsing file /Users/michael/Dropbox (BGU)/massive data mining/ 2020/notebooks/datasets/the-marvel-universe-social-network/hero-network.csv
Parsing completed. Parsed 574467 lines in 0.334314 secs.
Name: 
Type: Graph
Number of nodes: 6426
Number of edges: 167219
Average degree:  52.0445

Now, let's write a function that returns the number of common friends of two vertices:

In [4]:
def common_friends(g,u,v):
    if not g.has_node(u) or not g.has_node(v):
        return None
    return set(g.neighbors(u)) & set(g.neighbors(v))

common_friends(g, 'SPIDER-MAN/PETER PAR', 'GREEN GOBLIN/NORMAN ')
Out[4]:
{'ABSORBING MAN/CARL C',
 'ANGEL IV',
 'ANGEL/WARREN KENNETH',
 'ANT-MAN II/SCOTT HAR',
 'ANT-MAN/DR. HENRY J.',
 'AURA/ANN HERD',
 'AVRIL, SALLY',
 'BATWING/',
 'BEAST/HENRY &HANK& P',
 'BENDIX, DR. ALBERT',
 'BIG MAN/FREDERICK FO',
 'BLACK KING/SEBASTIAN',
 'BLACK KNIGHT IV/PROF',
 'BLACK KNIGHT V/DANE ',
 'BLACK TARANTULA II/C',
 'BLACKOUT/MARCUS DANI',
 'BLIZZARD/PROF. GREGO',
 'BROMWELL, DR.',
 'BROWN, MINDY MCPHERS',
 'BUSHKIN, BARNEY',
 'BUZZ',
 'CACHES, PROF. MARINA',
 'CAGE, LUKE/CARL LUCA',
 'CALYPSO',
 'CAPTAIN AMERICA',
 'CAPTAIN MARVEL II/MO',
 'CAPTAIN MARVEL/CAPTA',
 'CARNAGE/CLETUS KASAD',
 'CHALIZ, HAZRAM',
 'CHALIZ, SHANAZ RAYNA',
 'CHALIZ, TABRIAZ',
 'CHAN, SARAH',
 'CHERRYH, RANDOLPH WI',
 'CHESBRO',
 'CICERO, CAESAR BIG C',
 'CITIZEN V II/HELMUT ',
 'COLLECTOR/TANELEER T',
 'CONUNDRUM/',
 'CREEP/',
 'CRIME-MASTER/NICK LU',
 'CROMWELL, HAMILTON',
 'CYCLOPS/SCOTT SUMMER',
 'DAREDEVIL/MATT MURDO',
 'DEATH',
 'DELILAH',
 'DIPALMA, JOHNNY',
 'DR. DOOM/VICTOR VON ',
 'DR. DRUID/ANTHONY LU',
 'DR. OCTOPUS II/CAROL',
 'DR. OCTOPUS/OTTO OCT',
 'DR. STRANGE/STEPHEN ',
 'DRACULA/VLAD TEPES',
 'DRAX/ARTHUR DOUGLAS',
 'EENY',
 'ELLIS, KEN',
 'EVANS, DORIS',
 'EXECUTIONER II/SKURG',
 'FANCY DAN',
 'FIELDING, CLARENCE',
 'FIXER II/PAUL NORBER',
 'FORTUNATO',
 'FORTUNATO, GIACOMO J',
 'FRANKLIN, JEREMY',
 'FREDERICKSON, KOLINA',
 'GANTRY',
 'GARRISON, DR.',
 'GAUNT/MENDEL STROMM',
 'GIBBON/MARTIN BLANK',
 'GOTTFRIED, WILLIS',
 'GRANDMASTER/EN DWI G',
 'GRANT, GLORIA GLORY',
 'GREEN GOBLIN V/',
 'GRIZZLY/MAXWELL MARK',
 'HAMMERHEAD',
 'HAWK',
 'HIBBERT, HOPE',
 'HILLIARD, JACOB',
 'HOBGOBLIN II/RODERIC',
 'HOGAN, HAROLD J. HAP',
 'HOGAN, VIRGINIA PEPP',
 'HOWARD, PROFESSOR MA',
 'HULK/DR. ROBERT BRUC',
 'HUMAN TORCH/JOHNNY S',
 'ICEMAN/ROBERT BOBBY ',
 'INVISIBLE WOMAN/SUE ',
 'IONELLO, JASON',
 'IRON MAN IV/JAMES R.',
 'IRON MAN/TONY STARK ',
 "JACK O'LANTERN II/MA",
 'JACKAL/MILES WARREN',
 'JAMESON, COL. JOHN ',
 'JAMESON, J. JONAH',
 'JAMESON, MARLA MADIS',
 'JARVIS, EDWIN ',
 'JOCASTA',
 'KAFKA, DR. ASHLEY',
 'KELLEHER, MARY',
 'KELLY, SEN. ROBERT E',
 'KINGPIN/WILSON FISK',
 'KINGSLEY, DANIEL',
 'KORVAC, MICHAEL',
 'KRAVEN THE HUNTER II',
 'KRAVEN THE HUNTER/SE',
 'LANG, CASSANDRA CASS',
 'LEEDS, BETTY BRANT',
 'LEEDS, NED',
 'LEVINE, ABBY',
 'LEWIS, DEVON',
 'LEWIS, SHIRLEY WASHI',
 'LOBO, LUCKY',
 'LOOTER/NORTON G. FES',
 'LYJA LAZERFIST [SKRU',
 'MADAME WEB/CASSANDRA',
 'MAGGOTT/JAPHETH',
 'MAGUIRE',
 'MANDARIN',
 'MARROW/SARAH ',
 'MARVEL GIRL/JEAN GRE',
 'MAXWELL, MORRIS',
 'MCKEEVER, TINY',
 'MEANY',
 'MEDUSA/MEDUSALITH AM',
 'MELTER/BRUNO HORGAN',
 'MENKEN, DONALD L.',
 'MOCKINGBIRD/DR. BARB',
 'MODOK/GEORGE TARLETO',
 'MOLTEN MAN/MARK RAXT',
 'MONGRAIN, ALISON',
 'MONTANA',
 'MOON KNIGHT/MARC SPE',
 'MR. FANTASTIC/REED R',
 'MYSTERIO/QUENTIN BEC',
 'NELSON, FRANKLIN FOG',
 'NIGHTHAWK II/KYLE RI',
 'NITRO/ROBERT L. HUNT',
 'OMEGA II',
 'ORBIT',
 'OSBORN, EMILY',
 'OSBORN, HARRY',
 'OSBORN, LIZ ALLAN',
 'OSBORN, NORMAN',
 'OSBORN, NORMIE',
 'OVERRIDE/DR. GREGORY',
 'OX',
 'PARKER, MAY',
 'PARKER, MAY II',
 'POGUE, BARTON',
 'POWER MAN/ERIK JOSTE',
 'PROFESSOR ANGST/DR. ',
 'PROWLER/HOBIE BROWN',
 'PSYLOCKE/ELISABETH B',
 'PUNISHER II/FRANK CA',
 'RADIOACTIVE MAN/DR. ',
 'RALEIGH, RICHARD',
 'REYES, DR. CECELIA',
 'RICHARDS, FRANKLIN B',
 'ROBERTSON, JOE',
 'ROBERTSON, MARTHA',
 'ROBERTSON, RANDY',
 'ROGUE /',
 'ROSE IV/JACOB JAKE C',
 'ROSS, GEN. THADDEUS ',
 'SABRA/RUTH BAT-SERAP',
 'SANDMAN/WILLIAM BAKE',
 'SATELLITE',
 'SAWYER, RYAN',
 'SCORCHER/',
 'SCRIER II/S. CRIER',
 'SCRIER JR.',
 'SHARPE, ROSALINDE',
 'SHE-HULK/JENNIFER WA',
 'SHOTGUN III/',
 'SILVER SURFER/NORRIN',
 'SILVERMANE/SILVIO MA',
 'SIR',
 'SLOAN, ANNA',
 'SMASHER II',
 'SMITH, JOE',
 'SMYTHE, SPENCER',
 'SNIDER, DET. LOU',
 'SNIPES, DET.',
 'SNOW, CHARLIE',
 'SNOWBIRD/NARYA/ANNE ',
 'SOUSA, PAUL',
 'SPIDER-MAN CLONE/BEN',
 'SPIDER-MAN III/MARTH',
 'STACY, ARTHUR',
 'STACY, CAPT. GEORGE',
 'STACY, GWEN',
 'STACY, HELEN',
 'STACY, JILL',
 'STACY, NANCY',
 'STACY, PAUL',
 'STAHL, VERONICA',
 'STORM/ORORO MUNROE S',
 'SUB-MARINER/NAMOR MA',
 'SUNDOWN/DR. DAVE LOW',
 'SWORDSMAN/JACQUES DU',
 'TERRAX II/TYROS',
 'THING/BENJAMIN J. GR',
 'THOMPSON, EUGENE FLA',
 'THOMPSON, HARRISON',
 'THOMPSON, JESSICA JE',
 'THOMPSON, ROSIE',
 'THOR/DR. DONALD BLAK',
 'TIGRA/GREER NELSON',
 'TILBY, TRISH/PATRICI',
 'TITANIA II/MARY SKEE',
 'TORK, LT.',
 'TRAINER, DR. SEWARD',
 'TRAPSTER/PETER PETRU',
 'TREVANE, DET. CONNOR',
 'URICH, BEN',
 'URICH, DORIS',
 'VACUUM',
 'VERMICELLI, GIANNI',
 'VULTURE II/BLACKIE D',
 'VULTURE/ADRIAN TOOME',
 'WALTERS, BILLY',
 'WASP/JANET VAN DYNE ',
 'WATSON, ANNA',
 'WATSON-PARKER, MARY ',
 'WHITE QUEEN/EMMA FRO',
 'WHITE RABBIT',
 'WILKS, SHANTAL',
 'WOLVERINE/LOGAN ',
 'WONDER MAN/SIMON WIL',
 'WONG',
 'X-MAN/NATHAN GREY ',
 'YIN, ANGELA',
 'ZARRKO, ARTHUR'}

Now let's write a simple function that ranks friends-of-friends according to the number of common friends:

In [5]:
import operator
def find_friends_of_friends(g, v):
    "Returns a set with all the friends of friends of v that are not friends of v"
    friends_set = set(g.neighbors(v))
    friends_of_friends_set = set()
    for f in friends_set:
        friends_of_friends_set |= set(g.neighbors(f))
    friends_of_friends_set -= friends_set
    friends_of_friends_set -= {v}
    return friends_of_friends_set


def rank_friends_of_friends(g, v):
    rank_dict = {}
    vertices_set = find_friends_of_friends(g, v)
    for u in vertices_set:
        rank_dict[u] = len(common_friends(g, v, u))
    sorted_d = sorted(rank_dict.items(), key=operator.itemgetter(1), reverse=True)
    return sorted_d

rank_friends_of_friends(g, 'ANT-MAN/DR. HENRY J.')
Out[5]:
[('KA-ZAR/KEVIN PLUNDER', 197),
 ('SUNSPOT/ROBERTO DACO', 184),
 ('RHINO', 171),
 ('HIGH EVOLUTIONARY/HE', 171),
 ("SHANNA/SHANNA O'HARA", 170),
 ('PALADIN/PAUL DENNIS', 168),
 ('PHOENIX III/RACHEL S', 163),
 ('GUARDIAN/JAMES MACDO', 160),
 ('SABRA/RUTH BAT-SERAP', 152),
 ('SNOWBIRD/NARYA/ANNE ', 152),
 ('MACTAGGERT, MOIRA KI', 150),
 ('HELA [ASGARDIAN]', 149),
 ('JUBILEE/JUBILATION L', 144),
 ('BEYONDER', 143),
 ('ZABU', 143),
 ('WARLOCK III', 140),
 ('TORPEDO III/BROCK JO', 140),
 ('URSA MAJOR/MIKHAIL U', 137),
 ('CYPHER/DOUG RAMSEY', 137),
 ('LOCKHEED', 135),
 ('MIRAGE II/DANIELLE M', 133),
 ('BLITZKRIEG/', 131),
 ('ROM, SPACEKNIGHT', 131),
 ('BOOMERANG/FRED MYERS', 129),
 ('MEGGAN', 128),
 ('BALLANTINE, KAYLA', 126),
 ('ARABIAN KNIGHT/ABDUL', 126),
 ('SHAMROCK/MOLLY FITZG', 125),
 ('BOOMER/TABITHA SMITH', 124),
 ('GHOST RIDER III/DAN ', 120),
 ('MAGIK/ILLYANA RASPUT', 119),
 ('BROTHER VOODOO/DANIE', 116),
 ('SIRYN/THERESA ROURKE', 116),
 ('AJAK/TECUMOTZIN [ETE', 115),
 ('GEE/ALEX POWER', 114),
 ('DEFENSOR', 112),
 ('COLLECTIVE MAN', 112),
 ('NIGHTHAWK/KYLE RICHM', 111),
 ('GREY GARGOYLE/PAUL P', 107),
 ('BATROC/GEORGES BATRO', 106),
 ('LANG, CASSANDRA CASS', 106),
 ('DEATHLOK III/MICHAEL', 106),
 ('VALINOR', 104),
 ('BATTLESTAR/LEMAR HOS', 103),
 ('PEREGRINE, LE/FRANCK', 103),
 ('SABRETOOTH/VICTOR CR', 102),
 ('REAGAN, RONALD', 101),
 ("KARMA/XI'AN COY MANH", 101),
 ('MAGMA/AMARA AQUILLA/', 101),
 ('MAN-THING/THEODORE T', 101),
 ('LONGSHOT', 99),
 ('COUNTERWEIGHT II/KAT', 99),
 ('BRIDGE, GEORGE WASHI', 99),
 ('STRANGER', 99),
 ('COUNTERWEIGHT/JACK P', 98),
 ('LIGHTSPEED/JULIE POW', 98),
 ('DRUMM, JERICHO', 97),
 ('ZIRAN', 95),
 ('AMERICAN EAGLE III/J', 95),
 ('LIVING MUMMY', 94),
 ('SUMMERS, MADELYNE MA', 94),
 ('BLACK MAMBA/TANYA SE', 93),
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 ('RUNCITER, GAIL', 26),
 ('MAELEN', 26),
 ("CH'THON", 26),
 ('TRAINER, DR. SEWARD', 26),
 ('BLESSING, MORGANA', 26),
 ('BUSHWACKER/CARL', 26),
 ("MIDNIGHT/M'NAI", 26),
 ('CAPTAIN AMERICA IV/S', 26),
 ('BAILEY, SARA GREY', 26),
 ('RED RAVEN II', 26),
 ('AWESOME ANDROID', 26),
 ('EGO-PRIME', 26),
 ('IMPALA/', 26),
 ('DIAMONDHEAD/DYKER', 26),
 ('MALICE IV', 26),
 ('HAROKIN [ASGARDIAN]', 26),
 ('MOREAU, DAVID', 26),
 ('FATALE/PAMELA GREENW', 26),
 ('UNION JACK II/BRIAN ', 26),
 ('RANDOM II/MARSHALL E', 25),
 ('KNOCKOUT', 25),
 ('GREENSONG', 25),
 ('TOPSPIN/DARREN MITCH', 25),
 ('SHALLA BAL II', 25),
 ('KRAMER, KENNY', 25),
 ('SERESTUS', 25),
 ('DEATHLOK/COLONEL LUT', 25),
 ('KID REAPER', 25),
 ('JONES, BART', 25),
 ('MINDBLAST', 25),
 ('BUCKY II/FRED DAVIS', 25),
 ('TOWER', 25),
 ('SMITH, MARTY LOUIS L', 25),
 ('GOLDFIRE/AMEIKO SABU', 25),
 ('FEROCIA/', 25),
 ('HALE, JONAS', 25),
 ('DELPHIS, SIR', 25),
 ('PIROUETTE/JOBETH', 25),
 ('HIBBERT, HOPE', 25),
 ('CONKLIN, CAPT.', 25),
 ('HARVEST/CHI LO', 25),
 ('KWAN, TERRY', 25),
 ('NIGHT RIDER', 25),
 ('CHASIN, MAJOR', 25),
 ('QUAGMIRE', 25),
 ('BATTLEAXE/', 25),
 ('CYBELE [ETERNAL]', 25),
 ('NEPTUNE [OLYMPIAN]', 25),
 ('BRIGHTSWORD/CARTER D', 25),
 ('KAMAL', 25),
 ('GENII/JASON KIMBALL', 25),
 ('DELGADO, FATHER', 25),
 ('PUNCHOUT', 24),
 ('AXE/', 24),
 ('MANH, COY NGA', 24),
 ('GEIRRODUR', 24),
 ('MIRACLE MAN', 24),
 ('DARK CRAWLER', 24),
 ('STARK, HOWARD', 24),
 ('ANELLE', 24),
 ('GRAY, CHARLES', 24),
 ('ANDERSON, CHIEF MAGI', 24),
 ('JADE DRAGON/DEI GUAN', 24),
 ('AVIA', 24),
 ('CHESNEY, SANFORD', 24),
 ('DONALDSON, LINDA', 24),
 ('LOCKE, DR. ALEXANDER', 24),
 ('BOUDREAUX, BELLA DON', 24),
 ('CHEEVER, JOHN', 24),
 ('HRIMHARI', 24),
 ('LEVIATHAN II/EDWARD ', 24),
 ("EMPRESS R'KILL [SKRU", 24),
 ('SWITZLER, BEVERLY', 24),
 ('MAHA YOGI', 24),
 ('KINGSLEY, DANIEL', 24),
 ('MANH, COY LEONG XUI', 24),
 ('ADVERSARY', 24),
 ('SHOOTER, JIM', 24),
 ('BROWN, MINDY MCPHERS', 24),
 ('CALDRONE, JAVIER', 24),
 ('SUNSHINE/AUTUMN MACR', 24),
 ('MARTHA', 24),
 ('MAVERICK/CHRISTOPHER', 24),
 ('CAESAR, JONATHAN', 24),
 ('STINGER III/', 24),
 ('TWAKI, DR. TED', 24),
 ('CONAN, NEAL', 24),
 ('TURK', 24),
 ('ULTIMATOR/', 24),
 ('TANAKA, OPAL', 23),
 ('MARTINEZ, ISADORA', 23),
 ('URANIUM', 23),
 ('BLUE STREAK/', 23),
 ('CASSADA, ROBERTO', 23),
 ('CHEN', 23),
 ('SPAULDING, PETER', 23),
 ('CYCLONE', 23),
 ('CASSADA, CARLOS', 23),
 ('LIPSCOMB, DR. ANGELA', 23),
 ('GALEN KOR, ADMIRAL [', 23),
 ('CADMIUM', 23),
 ('CASSADA, MICHAEL', 23),
 ('JONES, LOUISE', 23),
 ('CALIFORNIUM', 23),
 ('CUTTHROAT/DANIEL LEI', 23),
 ('YMIR', 23),
 ('FORCE/CLAYTON WILSON', 23),
 ('THORSON, DR. WALTER', 23),
 ('SHAKATI', 23),
 ('SILVER II', 23),
 ('CHURCHILL, LORD', 23),
 ('BARON BRIMSTONE/', 23),
 ('KRAKOA', 23),
 ('PAINTER/WILHELM VON ', 23),
 ('MOLTEN MAN/MARK RAXT', 23),
 ('AGINAR', 23),
 ('KHANDRUVITCH, DR. VA', 23),
 ('FIREBRAND II/BROXTEL', 23),
 ('BERYLLIUM', 23),
 ('TRASK, LAWRENCE LARR', 23),
 ('SATAN/MARDU KURIOS', 23),
 ('JASON II', 23),
 ('LUMPKIN, WILHEMINA B', 23),
 ('BAMBI', 23),
 ('VOLLA', 23),
 ('PLUTONIUM', 23),
 ('DESIGNATE/TARENE', 23),
 ('CHANGELING', 23),
 ('EVANS, DORIS', 23),
 ('SPARK', 23),
 ('OX', 23),
 ('HORUS [EGYPTIAN GOD]', 23),
 ('FAIRMONT, AMANDA MAN', 23),
 ('KYMRI', 23),
 ('GOLD', 23),
 ('SLAB/KRIS ANDERSON', 23),
 ('HESCAMAR/MARNOT [ASG', 23),
 ("G'ANYM", 23),
 ('SIGYN', 23),
 ('EIGHTBALL', 23),
 ('SAROYAN, NEAL', 23),
 ('EVERY-MAN', 23),
 ('VERMIN/EDWARD WHELAN', 23),
 ('KID COLT', 23),
 ('RADIUM', 23),
 ('PLAGUE', 23),
 ('RAVEN, LT. JACOB', 23),
 ('NEPTUNIUM', 23),
 ('CESIUM', 23),
 ('BRUTUS', 23),
 ('PERRIKUS', 23),
 ('PINK PEARL/', 23),
 ('EUROPIUM', 23),
 ('ARIZE', 23),
 ('MUTANT MASTER', 23),
 ('JOSTEN, LINDY', 23),
 ('MACINTOSH, ANN', 23),
 ('RAMROD II', 23),
 ('CHANCE II/NICHOLAS P', 23),
 ('DRAGONESS', 23),
 ('RIDER, CHARLES', 23),
 ('HULK ROBOT', 22),
 ('BRICKMAN, SEN. MILES', 22),
 ('OMEGA-20/EUGENE', 22),
 ('SMUGGLER II/CONRAD J', 22),
 ('NAMORA/AQUARIA NAUTI', 22),
 ('BARIUM', 22),
 ('NORTH, DAKOTA', 22),
 ('METEORITE III/VALERI', 22),
 ("O'NEIL, BRIDGET", 22),
 ('ERBIUM', 22),
 ('SODIUM', 22),
 ('GRANT, SILAS', 22),
 ('REPTILLA', 22),
 ('INDIUM', 22),
 ('IKTHON', 22),
 ('DIONYSUS [GREEK GOD]', 22),
 ('SCANDIUM', 22),
 ('HYDRON', 22),
 ('MAINFRAME II', 22),
 ('RINGER/ANTHONY TONY ', 22),
 ('VYREK', 22),
 ('FORTUNATO', 22),
 ('KOFI [KYMELLIAN]', 22),
 ('NUKE', 22),
 ('ASTRA II', 22),
 ('PUNISHER', 22),
 ('DARKOTH/MAJOR DESMON', 22),
 ('BLACK FOX II/', 22),
 ('THORNN', 22),
 ('JACK FROST/', 22),
 ('CHLORINE', 22),
 ('PHOSPHORUS', 22),
 ('BAXTER, NOAH', 22),
 ('BRUTACUS', 22),
 ('DISPROSIUM', 22),
 ('BLITZIANA', 22),
 ('KANGAROO II/', 22),
 ('THALLIUM', 22),
 ('CANDY', 22),
 ('SCREAM', 22),
 ('TREVANE, DET. CONNOR', 22),
 ('SHAPER OF WORLDS', 22),
 ('PIECEMEAL/GILBERT BE', 22),
 ('VAKUME', 22),
 ('BANKS, SHARON', 22),
 ('STEEL SERPENT', 22),
 ('HOCHBERG, ROGER', 22),
 ('GROVEL', 22),
 ('ZIRCONIUM', 22),
 ('COBALT', 22),
 ('HARNESS/ERIKA BENSON', 22),
 ('HARGEN', 22),
 ('HELIX/RAFAEL CARAGO', 22),
 ('CAT III/SHEN KUEI', 22),
 ('RANDI', 22),
 ('MAN-MOUNTAIN MARKO', 22),
 ('RALSTON, SENATOR ROB', 22),
 ('PROSH', 22),
 ('SCOURGE II/', 22),
 ('DRAKE, MADELINE BEAT', 22),
 ('VERTIGO', 22),
 ('ARSENIC', 22),
 ('NEVILLE, KATE', 22),
 ('ZELDA', 22),
 ('NEFFETHESK', 22),
 ('RAMBO', 22),
 ('CARBON', 22),
 ('CAPTAIN AMERICA V/RO', 22),
 ('TRANTRA', 22),
 ('PUENTES, DONNA MARIA', 22),
 ('REE', 22),
 ('VALKYRIE IV', 21),
 ('MICHEL, RAFE', 21),
 ('YRDISIS', 21),
 ('HEMINGWAY', 21),
 ('SUWAN', 21),
 ('KAMIKAZE', 21),
 ('HEALER', 21),
 ('TAKAHAMA, ROBERT', 21),
 ('LADONNA, HOLLY', 21),
 ('YANDROTH', 21),
 ('SHARD', 21),
 ('STONE, CHANTAL', 21),
 ('TAKAHAMA, JANE', 21),
 ('GREEN GOBLIN IV/PHIL', 21),
 ('PAPA HAGG', 21),
 ('TOPAZ', 21),
 ('RALF', 21),
 ('POTTER, BETSY BEATTY', 21),
 ('GHOST', 21),
 ('CABER', 21),
 ('ERG', 21),
 ('DMITRI', 21),
 ('PARKER, BEN', 21),
 ('GAZELLE', 21),
 ('CRIPPLER/KARL STRIKL', 21),
 ('SATURNYNE, OPAL LUNA', 21),
 ('MEACHUM, WARD', 21),
 ('ALCHEMY/THOMAS JONES', 21),
 ('RIDER, GLORIA', 21),
 ('GUDRUN [ASGARDIAN]', 21),
 ('ELSIE DEE', 21),
 ('FUNARO, DONNA', 21),
 ('KLIGGER/SEN. EUGENE ', 21),
 ('HATCHWAY, VERNON', 21),
 ('ASKANI', 21),
 ('STRANG, COL./LT. GAR', 21),
 ('APE/', 21),
 ('MONTANA', 21),
 ('HARDY, STAN', 21),
 ('CRAWLEY, BETRAND', 21),
 ('HILARY', 21),
 ('CERANDA', 21),
 ('ANAK', 21),
 ('ALBERT', 21),
 ('FANCY DAN', 21),
 ('COBALT MAN', 21),
 ('TOY', 20),
 ('JONES, LORRAINE LORR', 20),
 ('JONES, TAMMY ANNE', 20),
 ('QUINN, ASHLEY', 20),
 ...]
In [6]:
rank_friends_of_friends(g,'HULK/DR. ROBERT BRUC')
Out[6]:
[('SUNSPOT/ROBERTO DACO', 170),
 ('KINGPIN/WILSON FISK', 168),
 ('KNIGHT, MISTY', 158),
 ('MACTAGGERT, MOIRA KI', 147),
 ('COBRA/KLAUS VORHEES', 136),
 ('TOAD/MORTIMER TOYNBE', 133),
 ('BOOMER/TABITHA SMITH', 130),
 ('DIABLO/ESTEBAN DE AB', 128),
 ('RED SKULL/JOHANN SCH', 127),
 ('HYDROMAN/MORRIE BENC', 127),
 ('MEGGAN', 126),
 ('GEE/ALEX POWER', 126),
 ('KENT, CLARK', 121),
 ('CARTER, SHARON', 119),
 ('SPIDER-MAN CLONE/BEN', 118),
 ('COUNTERWEIGHT II/KAT', 116),
 ('COUNTERWEIGHT/JACK P', 113),
 ('LIGHTSPEED/JULIE POW', 113),
 ('THENA', 112),
 ('WING, COLLEEN', 111),
 ('CORSAIR', 107),
 ('SIRYN/THERESA ROURKE', 106),
 ('FORGOTTEN ONE/GILGAM', 105),
 ('DESTINY II/IRENE ADL', 105),
 ('DIAMONDBACK II/RACHE', 104),
 ('KRAVEN THE HUNTER/SE', 100),
 ('SPIRAL', 99),
 ('RADIOACTIVE MAN/DR. ', 99),
 ('ROSENTHAL, BERNIE', 98),
 ('LANG, CASSANDRA CASS', 98),
 ('SHOCKER/HERMAN SCHUL', 95),
 ("CH'OD", 95),
 ('SUMMERS, MADELYNE MA', 95),
 ('CITIZEN V III/DALLAS', 95),
 ("DEATHBIRD [SHI'AR]", 94),
 ('RAZA LONGKNIFE', 94),
 ('DAYTRIPPER/AMANDA SE', 93),
 ('FREEMAN, DUANE JEROM', 92),
 ('NOMAD III/JACK MONRO', 92),
 ('TURBO II (A)/MICHIKO', 92),
 ('VANISHER', 91),
 ("MAM'SELLE HEPZIBAH", 91),
 ('DARK BEAST/HENRY MCC', 89),
 ('KINCAID, DR. KEITH', 89),
 ('POWER, DR. JIM', 88),
 ('MASTER MOLD II', 88),
 ('IMPOSSIBLE MAN', 88),
 ('TANAKA, KENJIRO', 87),
 ('MARRINA/MARRINA SMAL', 87),
 ('CALIBAN/', 87),
 ('BLACK MAMBA/TANYA SE', 86),
 ('POLARIS DOPPELGANGER', 84),
 ('ANACONDA/BLANCHE SIT', 84),
 ('BLACK KNIGHT IV/PROF', 84),
 ('KELLY, SEN. ROBERT E', 83),
 ('DOPPELGANGER', 83),
 ('EON', 83),
 ('STILT-MAN/WILBUR DAY', 82),
 ('SKIN/ANGELO ESPINOSA', 82),
 ('RICTOR/JULIO ESTEBAN', 82),
 ('CUSHING, KATE', 81),
 ('STONE, LT. MARCUS', 81),
 ('FIREFIST/RUSTY COLLI', 81),
 ('GREY, DR. JOHN', 81),
 ('SILVER SAMURAI/KENYU', 81),
 ('EEL II/EDWARD LAVELL', 79),
 ('SCANNER II', 79),
 ('CARGIL/JOANNA CARGIL', 79),
 ('LEECH', 79),
 ('HUSK/PAIGE GUTHRIE', 78),
 ('MADDICKS, ARTHUR ART', 78),
 ('POWER, MARGARET', 77),
 ('DOMINO III/BEATRICE/', 77),
 ('QUASAR DOPPELGANGER', 76),
 ('FOREIGNER/RAFAEL BAS', 76),
 ('KARNAK DOPPELGANGER', 76),
 ('HULK DOPPELGANGER', 76),
 ('PROFESSOR X DOPPELGA', 76),
 ('HAMMER, JUSTIN', 76),
 ('PUCK DOPPELGANGER', 76),
 ('BLACK WIDOW DOPPELGA', 76),
 ('THING DOPPELGANGER', 76),
 ('MAELSTROM/MALCOLM ST', 76),
 ('LIBRA/GUSTAV BRANDT', 76),
 ('TRITON DOPPELGANGER', 76),
 ('BEAST DOPPELGANGER', 76),
 ('NOVA DOPPELGANGER', 76),
 ('BLACK KNIGHT IV DOPP', 76),
 ('SYNCH/EVERETT THOMAS', 76),
 ('VISION DOPPELGANGER', 76),
 ('CALLISTO', 75),
 ('CRIMSON COWL II/', 75),
 ('LANE, LOIS', 75),
 ('LUBENSKI, NATE', 75),
 ('GRAVITON/FRANK HALL', 74),
 ('CORTEZ, FABIAN', 74),
 ('WATER WIZARD/PETE', 73),
 ('TASKMASTER', 73),
 ('DRAGONFLY/VERONICA D', 72),
 ("CR'REEE", 72),
 ('BANNON, LANCE', 72),
 ('ZARRKO, ARTHUR', 72),
 ('SCORPION/MACDONALD G', 71),
 ('SIKORSKY', 71),
 ('TERRAX II/TYROS', 71),
 ('NITRO/ROBERT L. HUNT', 71),
 ('CHAMBER/JONOTHON STA', 71),
 ('BOVA', 71),
 ('ROMA', 70),
 ('LEGION II/DAVID HALL', 70),
 ('TESSA', 70),
 ('ASP II/CLEO', 70),
 ('EXODUS/BENNET DUPARI', 70),
 ('SENTINELS', 69),
 ('FRENCHIE/JEAN-PAUL D', 69),
 ('MASTERSON, KEVIN', 69),
 ('MADAME MASQUE/GIULIE', 69),
 ('COUNT NEFARIA, LUCHI', 69),
 ('RICHARDS, NATHANIEL', 69),
 ('SHATTERSTAR II/GAVEE', 68),
 ('SILVERMANE/SILVIO MA', 68),
 ('REJECT/RAN-SAK [DEVI', 68),
 ('FRIEDLANDER, SHARON', 68),
 ('SCARECROW/EBENEZER L', 68),
 ('SAXON, STARR', 68),
 ('WARSTAR', 68),
 ('KIRBY, JACK', 67),
 ('CORSI, TOM', 67),
 ('STRUCKER, ANDREA', 67),
 ('GREY, ELAINE', 67),
 ('STRUCKER, ANDREAS', 67),
 ("S'YM", 66),
 ('AUSTIN, SUSAN', 66),
 ('SAURON/DR. KARL LYKO', 66),
 ('WISDOM, PETER', 65),
 ('RUIZ, RIGGER', 65),
 ('JONES, CHARLOTTE', 65),
 ('UNICORN/MYLOS MASARY', 64),
 ('FERAL/MARIA CALLASAN', 64),
 ('ARKON', 64),
 ('TEMPEST/GRANNZ', 64),
 ('CHENEY, LILA', 63),
 ('HILDY [ASGARDIAN]', 63),
 ('BLACKOUT/MARCUS DANI', 63),
 ('DEATH ADDER', 62),
 ('NEKRA/ADRIENNE HATRO', 62),
 ('RATTLER', 61),
 ('ULIK', 61),
 ('HUNTER, STEVIE', 61),
 ('HAMMERHEAD', 61),
 ('SMASHER IV', 61),
 ("MAN-APE/M'BAKU", 61),
 ('GATEWAY', 61),
 ('STARBOLT', 61),
 ('SIDEWINDER/SETH VOEL', 61),
 ('ROBERTSON, RANDY', 61),
 ('HOBGOBLIN II/RODERIC', 60),
 ('HALLER, GABRIELLE', 60),
 ('MERCADO, JOY', 60),
 ('MUGGINS, MAMIE', 60),
 ('DEADPOOL/JACK/WADE W', 60),
 ('MR. SINISTER/NATHAN ', 59),
 ('FLYING TIGER', 59),
 ('EARTHQUAKE', 58),
 ("VIBRAXAS/N'KANO", 58),
 ('BARON MORDO/KARL MOR', 58),
 ('MODRED THE MYSTIC', 58),
 ('FRIGGA', 58),
 ('TITAN', 58),
 ('CROSSBONES/BROCK BIN', 58),
 ('STEELE, MARCY MASTER', 58),
 ('VOGHT, AMELIA', 58),
 ('PHOBIUS', 58),
 ('SPRITE', 58),
 ('RASSITANO, MAD DOG', 58),
 ('DREADKNIGHT/BRAM VEL', 58),
 ('REYES, DR. CECELIA', 58),
 ("N'ASTIRH", 57),
 ('NUKLO/ROBERT FRANK J', 57),
 ('HELIO', 57),
 ('BALDWIN, MADELYNE MA', 57),
 ('CLAREMONT, CHRIS', 57),
 ('SCHEMER/RICHARD FISK', 57),
 ('SUPER SABRE/MARTIN F', 57),
 ('SHADOW KING/AMAHL FA', 57),
 ('HODGE, CAMERON', 57),
 ('QUICKSAND', 56),
 ('ARCLIGHT/PHILLIPA SO', 56),
 ('BROTHERS GRIMM II/BA', 55),
 ('VERTIGO II', 55),
 ('BUCKY/BUCKY BARNES', 55),
 ('SHAPE', 55),
 ('TORO/TOM RAYMOND', 55),
 ('HOBGOBLIN', 55),
 ("ERIC THE RED [SHI'AR", 54),
 ('AHAB/RORY CAMPBELL', 54),
 ('WHITMAN, DEBRA', 54),
 ('GRIFFIN II/JOHNNY HO', 54),
 ('LIVING MONOLITH/AHME', 54),
 ('BRIGHTWIND', 54),
 ('SHOCKWAVE/LANCASTER ', 54),
 ('LEAP-FROG/VINCENT PA', 54),
 ('BLACK TOM CASSIDY', 54),
 ('FORRESTER, CAPT. ALE', 53),
 ('HERA/AUGUSTINE JONES', 53),
 ('BLAQUESMITH', 53),
 ('YELLOW CLAW/TZING JA', 53),
 ('PETROVITCH, IVAN', 53),
 ('MESMERO', 53),
 ('WOLFE, SARA', 53),
 ('PENANCE/MONET ST. CR', 53),
 ('GRONK', 53),
 ('ARMADILLO/ANTONIO RO', 52),
 ('OSIRIS', 52),
 ('LADY DEATHSTRIKE/YUR', 52),
 ('ARCADE/', 52),
 ('ZURI', 51),
 ('LIVING LASER/ARTHUR ', 51),
 ('AMPHIBIUS', 51),
 ('SHA SHAN', 51),
 ('STRYFE', 50),
 ('SETH II', 50),
 ('LOCKLIN, GENERAL MER', 50),
 ('BLACKWING/JOSEPH MAN', 50),
 ('REPTYL', 50),
 ('OMEGA RED/ARKADY ROS', 50),
 ('RAMBEAU, MARIA', 50),
 ('BRAINCHILD', 50),
 ('CARPENTER, RACHEL', 49),
 ('ROCKET RACER/ROBERT ', 49),
 ('RUNNER', 49),
 ('PSYCHO-MAN', 49),
 ('HUNTARA/MARY ELIZABE', 49),
 ('ASTRA', 49),
 ('BALDWIN, JUSTIN', 49),
 ('ALRAUNE, MARLENE', 49),
 ('WALDO', 49),
 ('BOOMSLANG/', 48),
 ('WIDGET', 48),
 ('TRUTH', 48),
 ('BEAUTY', 48),
 ('ZERO', 48),
 ('MOJO', 48),
 ('LEWIS, SHIRLEY WASHI', 48),
 ('CHARM', 48),
 ('COPYCAT/VANESSA', 48),
 ('BLOODAXE/JACKIE LUKU', 48),
 ('RAHN, TAMARA', 48),
 ('HUSSAR', 48),
 ('PIERCE, DONALD', 48),
 ('SURTUR', 47),
 ('FLATMAN', 47),
 ('DINAH-SOAR', 47),
 ('SAPRISTI, JERRY', 47),
 ('MADCAP', 47),
 ('GAZA', 47),
 ('SCARFE, RAFAEL', 47),
 ('FIELSTEIN, FIREWORKS', 47),
 ('MERRYWEATHER, IRENE', 47),
 ('DOORMAN', 47),
 ('MR. IMMORTAL', 47),
 ('MOY, ALYSSA', 47),
 ('BUSHMASTER II/QUINCY', 47),
 ('BLAIRE, CARTER', 47),
 ('BARBARUS', 47),
 ('RANSOME, JENNIFER AM', 46),
 ('NEWELL, DIANE ARLISS', 46),
 ('CYCLONE II/PIERRE FR', 46),
 ('HOPKINS, STEVE', 46),
 ('BLACK CROW/JESSE BLA', 46),
 ('WETHERELL, MANOLI', 46),
 ('SUNDER', 46),
 ('KAINE', 46),
 ('STERMAN, ANDREA ANDI', 45),
 ('TWO-GUN KID', 45),
 ('GAUNTLET', 45),
 ('JACKSON, ANDREW JOCK', 45),
 ('KRAMER, STACEY', 45),
 ('MINERVA [KREE]', 45),
 ('YASHIDA, MARIKO', 45),
 ('FAIRMONT, HANNAH', 45),
 ('WHYTE, HARMONY (HARM', 45),
 ('RAMBEAU, FRANK', 45),
 ('MASTER KHAN/TYRONE K', 44),
 ('JONES, HUGH', 44),
 ('FOREARM', 44),
 ('BLIZZARD II/DONALD G', 44),
 ('SWORDSMAN II/COTATI', 44),
 ('TEZCATLIPOCA', 44),
 ('LELAND, HARRY', 44),
 ('BUTCH', 44),
 ('PIPELINE/CORMICK GRI', 44),
 ('COACHWHIP', 44),
 ('ITZAMNA', 44),
 ('RODGERS, MARIANNE', 44),
 ('SVAROG', 44),
 ('ELECTRON', 44),
 ('LUPO', 44),
 ('ARRANGER/', 44),
 ('NUADA', 44),
 ('MANITOU', 44),
 ('APOLLO [GREEK GOD]', 43),
 ('MARVEL BOY VI', 43),
 ('PUFF ADDER', 43),
 ('CHANG, DR. PHILLIP', 43),
 ('MAGGOTT/JAPHETH', 43),
 ('ROCK PYTHON', 43),
 ('KLEINSTOCK, HARLAN', 43),
 ('DEVLOR', 43),
 ('BUSH, GEORGE', 43),
 ('STONEWALL/LOUIS HAMI', 43),
 ('EITRI', 43),
 ('PIPER', 43),
 ('KLEINSTOCK, SVEN', 43),
 ('POWERSURGE/ILLARION ', 42),
 ('MORRELL, SABRINA', 42),
 ('WILDSIDE', 42),
 ('MALICE V/NAKIA', 42),
 ('TUSK', 42),
 ('GROWING MAN', 42),
 ('ZEMO/BARON HEINRICH ', 42),
 ('UMAR', 42),
 ('RICHARDS, FRANKLIN 2', 42),
 ('WALTERS, BILLY', 42),
 ('ULTIMUS', 42),
 ('M', 42),
 ('TORK, SGT.', 42),
 ('WOO, JIMMY', 42),
 ('HOLOCAUST', 42),
 ('TOMBSTONE/LONNIE THO', 42),
 ('RAYMOND, ANN', 42),
 ('QUILL', 41),
 ('SHARPE, ROSALINDE', 41),
 ('ADAMS, CINDY', 41),
 ('RED GUARDIAN II/ALEX', 41),
 ('GLADIATOR/MELVIN POT', 41),
 ('TYR', 41),
 ('CHEKOV, ARTHUR ANTON', 41),
 ('BLACK RACER/', 41),
 ('MISS LOCKE', 41),
 ('SPIDERCIDE', 41),
 ('NIGHTSIDE', 41),
 ('DARLA', 41),
 ('PALERMO, VICTOR', 41),
 ('PALERMO, HARRIET ROS', 41),
 ('DORREK [SKRULL]', 41),
 ('HARDRIVE', 41),
 ('MADAME MASQUE III', 41),
 ('VENUS/APHRODITE/VICT', 41),
 ('PSI-LORD/FRANKLIN BE', 40),
 ('BEDLAM/JESSE AARONSO', 40),
 ('TITANIUM MAN/BORIS B', 40),
 ('ROULETTE II/JENNY ST', 40),
 ('BUSHKIN, BARNEY', 40),
 ('SHAW, SHINOBI', 40),
 ('CREED, GRAYDON', 40),
 ('CENTURY', 40),
 ('LANG, DR. STEVEN', 40),
 ('CLARKE, GENERAL JERE', 40),
 ('IMPULSE', 40),
 ('MOREAU, PHILLIP', 40),
 ('AURIC/', 40),
 ('RASPUTIN, MIKHAIL', 40),
 ('4-D MAN/MERCURIO', 40),
 ('UNUSCIONE', 40),
 ('HERMOD [ASGARDIAN]', 40),
 ('PHASTOS [ETERNAL]', 40),
 ('MICROCHIP/LINUS LIEB', 40),
 ('CARDIAC/DR. ELI WIRT', 40),
 ('KANE, MARCY/KAINA', 40),
 ('OGRE', 40),
 ('KRAVEN THE HUNTER II', 40),
 ('BARON BLOOD/JOHN FAL', 40),
 ('ATLAS', 40),
 ('SPHINX', 40),
 ('FER DE LANCE/TERESA', 39),
 ('HARPOON', 39),
 ('STROBE/', 39),
 ('BOWEN, FATHER MICHAE', 39),
 ('EENY', 39),
 ('ATHENA [GREEK GOD]', 39),
 ('JACKSON, STEVE', 39),
 ('SILVER', 39),
 ('COPPERHEAD III/DAVIS', 39),
 ('FOXBAT', 39),
 ('KATZENBERG, NICK', 39),
 ('THAKOS', 39),
 ('STUART, DR. ALISTAIR', 39),
 ('MEANY', 39),
 ("JACK O'LANTERN II/MA", 39),
 ('ERWIN, MORLEY', 39),
 ('MAJOR DOMO', 39),
 ('CARINA/CARINA WALTER', 39),
 ('JETSTREAM/HAROUD IBN', 39),
 ('ZURAS', 39),
 ('KAMO THARNN', 38),
 ('SENTRY 459', 38),
 ('WHIPLASH II/', 38),
 ('MARVEL GIRL II/VALER', 38),
 ('BLACKBALL', 38),
 ('TAI II', 38),
 ('QUASAR II', 38),
 ('TANA NILE', 38),
 ('BLOOD WRAITH/SEAN DO', 38),
 ('JEMIAH', 38),
 ('FAMINE/AUTUMN ROLFSO', 38),
 ('SET', 38),
 ("ARAKI [SHI'AR]", 38),
 ('ENRAGED', 38),
 ('SWANN, DR. EVAN', 38),
 ('OWL/LELAND OWLSLEY', 38),
 ('MASTER PANDEMONIUM/M', 38),
 ('VALKIN', 38),
 ('CAMPBELL, DR. RORY', 38),
 ('KRUMA, TALIA', 38),
 ('GUNTHAR II', 37),
 ('HAYWIRE/', 37),
 ('SPRAGG', 37),
 ('RAM, SIR', 37),
 ('MODAM', 37),
 ('MONGOOSE', 37),
 ('SCALPHUNTER', 37),
 ('ROBERTSON, AMANDA', 37),
 ('PUDDLEGULP', 37),
 ('GATOR, LORD', 37),
 ('MASQUE', 37),
 ('SHELDON, PHIL', 37),
 ('MALUS, DR. KARL', 37),
 ('EMPATH/MANUEL ALFONS', 36),
 ('REAPER II', 36),
 ('PROFESSOR POWER/ANTH', 36),
 ('RAKSOR [SKRULL]', 36),
 ('WEIN, LEN', 36),
 ('STARK, MORGAN', 36),
 ('PENANCE III', 36),
 ('CARNAGE/CLETUS KASAD', 36),
 ('ANDERSSEN, TANYA', 36),
 ('IRON', 36),
 ('NOBILUS', 36),
 ('MENTOR II', 36),
 ('TREMONT, JONATHAN', 36),
 ('HRIMHARI', 36),
 ('WEBSTER, NORM', 36),
 ('HAFNIUM', 36),
 ('PSYNAPSE', 36),
 ('BEL-DANN, GEN.', 36),
 ('FLAG-SMASHER', 36),
 ('TSURAYABA, MATSUO', 36),
 ('PARETSKY, DR. GARY', 36),
 ('STUART, BRIG. ALYSAN', 36),
 ('PLUNDERER/LORD EDGAR', 36),
 ('STACY, CAPT. GEORGE', 36),
 ('HEYWERTH, GENERAL', 35),
 ('ARIES II/GROVER RAYM', 35),
 ('THUMBELLINA', 35),
 ('COLLINS, DEMITRIUS', 35),
 ('ROTH, ARNIE', 35),
 ('TAYLOR, LEILA', 35),
 ('URSULA, LADY', 35),
 ('CARDINAL/CLEMDENON', 35),
 ('MARILLA [INHUMAN]', 35),
 ('WHITE WOLF/HUNTER', 35),
 ('REESE, MURRAY', 35),
 ('HUXLEY, DR. ALDA', 35),
 ('DANSEN MACABRE', 35),
 ('HORTON, PROFESSOR PH', 35),
 ('STEELE, BOBBY', 35),
 ('HERMES [GREEK GOD]', 35),
 ('LLYRON', 35),
 ('ELEKTRA/ELEKTRA NATC', 35),
 ('NANNY II', 35),
 ('CALEDONIA/ALISANDE S', 35),
 ('RITTER, DEBORAH ', 35),
 ('TAGAR', 35),
 ('CATSEYE/SHARON SMITH', 35),
 ('ISHIMA, DAVID', 35),
 ('DIAMONDHEAD/DYKER', 35),
 ('SCRAMBLER', 35),
 ('RITTER, DONALD', 35),
 ('TAROT/MARIE-ANGE COL', 35),
 ('ORPHAN-MAKER/PETER', 35),
 ('WHITEOUT', 35),
 ('FUJIKAWA, RUMIKO', 35),
 ('GREENSONG', 34),
 ('BULLSEYE II/BENJAMIN', 34),
 ('FREEMAN, DEREK', 34),
 ('KULAN GATH', 34),
 ('RECORDER II', 34),
 ('BYRRAH [ATLANTEAN]', 34),
 ('FIN FANG FOOM/MIDGAR', 34),
 ('PEREZ, GEORGE', 34),
 ('EMPLATE/MARIUS ST. C', 34),
 ('DRAKE, WILLIAM ROBER', 34),
 ('BROWN, MINDY MCPHERS', 34),
 ('JENNINGS, JOSEPH', 34),
 ('SCORCHER/', 34),
 ('WILFORD, ELISABETH B', 34),
 ('BROMWELL, DR.', 33),
 ('COLE, WADE', 33),
 ('BIG BERTHA/ASHLEY CR', 33),
 ('TYGER, LORD', 33),
 ('THERAK', 33),
 ('GIDEON', 33),
 ('FRIDAY', 33),
 ('MADAME MENACE/SUNSET', 33),
 ('DARE, KATHLEEN', 33),
 ('ELLIS, KEN', 33),
 ('FARREL, MIKE', 33),
 ('WAR/ABRAHAM LINCOLN ', 33),
 ('ROSSI, COL. MICHAEL ', 33),
 ('CORD, EDWIN', 33),
 ('SUGAR MAN', 33),
 ("D'KEN [SHI'AR]", 33),
 ('MACON, ANGELO', 33),
 ('BOMBSHELL/', 32),
 ('PEARSON, MARCY', 32),
 ('KORATH THE PURSUER', 32),
 ('PLUNDER, ADAM KYLE M', 32),
 ('MAJOWSKI, SAM MOTHER', 32),
 ('SHATTERAX', 32),
 ('KID REAPER', 32),
 ('LEIR', 32),
 ('BYRNE, JOHN', 32),
 ('APE MAN/GORDON MONK ', 32),
 ('JOYCE, SAMANTHA', 32),
 ('KUBIK', 32),
 ('NELSON, CANDACE', 32),
 ('TERMINUS', 32),
 ('HILDEGARDE [ASGARDIA', 32),
 ('CICERO, CAESAR BIG C', 32),
 ('BLAKE, BECKY', 32),
 ('DORMA II [ATLANTEAN]', 32),
 ('KINCAID, JIMMY', 32),
 ('MOTHER NIGHT/SUSAN S', 32),
 ('NALES, DET. REBECCA ', 32),
 ('JOYSTICK/JANICE', 32),
 ('MAGNUS', 32),
 ('PROTEUS II', 32),
 ('ZALADANE', 32),
 ('STYGIAN STARBENDER', 32),
 ('THRENODY', 32),
 ('RIPTIDE', 32),
 ('SCRATCH, NICHOLAS', 32),
 ('BLOCKBUSTER III/MICH', 32),
 ('MERLIN', 32),
 ('FIREBRAND/GARY GILBE', 31),
 ('SYBYL DORN', 31),
 ('MUNIN', 31),
 ('BODYBAG', 31),
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 ('KATH', 31),
 ('MAGNUM, MOSES', 31),
 ('GATECRASHER', 31),
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 ('VINDALOO/', 31),
 ('LORD TEMPLAR/', 31),
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 ('NEOPHYTE/', 31),
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 ('SLOAN, DR.', 31),
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 ('BISON/BILLY KITSON', 31),
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 ('HAWKSHAW', 30),
 ('BLAZE, ROXANNE SIMPS', 30),
 ('WARD, SEN. STEWART', 30),
 ('SENYAKA', 30),
 ('KHORYPHOS [ETERNAL]', 30),
 ('COCKRUM, DAVE', 30),
 ('DOMO', 30),
 ('KINGO SUNEN [ETERNAL', 30),
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 ('MILAN', 30),
 ('MOONHUNTER/ZACK MOON', 30),
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 ('RAMPAGE/STUART CLARK', 30),
 ('MANH, COY LEONG XUI', 30),
 ('AEGIS/TREY ROLLINS', 30),
 ('LUMINOR', 30),
 ('CONNORS, BILLY', 30),
 ('ROSEN, WALTER', 30),
 ('BRICKMAN, SEN. MILES', 29),
 ('SHALLA BAL II', 29),
 ('CASSADA, ROBERTO', 29),
 ('DISCUS', 29),
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 ('CASSADA, CARLOS', 29),
 ('HOUND', 29),
 ('IRON CROSS/HELMUT GR', 29),
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 ('NEREEL', 29),
 ('CASSADA, MICHAEL', 29),
 ('HIROMITSU', 29),
 ('DAMIAN, DR. DANIEL', 29),
 ('TOPAZ', 29),
 ('ADORA', 29),
 ('BORA', 29),
 ('KRAKOA', 29),
 ('THOMAS, COMMANDER DA', 29),
 ('SHATTERFIST/', 29),
 ('BAMBI', 29),
 ('CANDY', 29),
 ('CHANGELING', 29),
 ('HARGEN', 29),
 ('TORG', 29),
 ('HIBBERT, HOPE', 29),
 ('RANDI', 29),
 ('VERMIN, LADY', 29),
 ('EXPEDITER II/ZOE CUL', 29),
 ('LADY OF THE LAKE', 29),
 ('NEUTRON', 29),
 ('JACKAL/MILES WARREN', 29),
 ('NELSON, DEBBIE HARRI', 29),
 ('ROSE IV/JACOB JAKE C', 29),
 ('TYPHOID MARY/MARY ME', 29),
 ('CALCULUS/JAHAHAREL P', 29),
 ('MAVERICK/CHRISTOPHER', 29),
 ('CHANEY, AMANDA', 29),
 ('CAESAR, JONATHAN', 29),
 ('ZARAN/MAXIMILLIAN ZA', 29),
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 ('TAKACHIHO, HIRO', 28),
 ('ATOR, GENERAL', 28),
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 ('AXE/', 28),
 ('DECAY II/YOSHIRO HAC', 28),
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 ('EELAK', 28),
 ('YMIR', 28),
 ('FREDERICKS, GENERAL', 28),
 ('JONES, BART', 28),
 ('BARON BRIMSTONE/', 28),
 ('SPIDER-MAN III/MARTH', 28),
 ('CRIMSON DYNAMO III/A', 28),
 ('RASPUTINA, ILLYANA I', 28),
 ('RAPUNZEL', 28),
 ('CONQUEST/BRIDGET MAL', 28),
 ('IMMORTUS, MARCUS', 28),
 ('CHALMERS, JUDGE ROBE', 28),
 ('MARGOYLE', 28),
 ('TAKU', 28),
 ('SHOOTER, JIM', 28),
 ('BAYMAX', 28),
 ('MALICE IV', 28),
 ('PULSSUS', 28),
 ('MARTHA', 28),
 ('MARTIN, TERI', 28),
 ('BOUNTY II', 28),
 ('GLABOO', 28),
 ('RIVERA, DR. JASON', 28),
 ('STURDY, PROF. KENNET', 28),
 ('JONES, LORRAINE LORR', 27),
 ('JONES, TAMMY ANNE', 27),
 ('ACHEBE, REVEREND DOC', 27),
 ('VIRGO/ELAINE MCLAUGH', 27),
 ('LEFT-WINGER/HECTOR L', 27),
 ('CANCER/JACK KLEVENO', 27),
 ('JONES, LOUISE', 27),
 ('SUPREMOR', 27),
 ('JACK FLAG/', 27),
 ('ARCADIUS', 27),
 ('ZON', 27),
 ('RIGHT-WINGER/JEROME ', 27),
 ('WIZ KID/TAKESHI MATS', 27),
 ('SUPERIA/WENTWORTH, D', 27),
 ('LEO/DANIEL RADFORD', 27),
 ('NAZE', 27),
 ('AVIA', 27),
 ('FIREBRAND II/BROXTEL', 27),
 ('SMITH, MARTY LOUIS L', 27),
 ('BLAKE, DR. DONALD (C', 27),
 ('DEAN, SUSAN', 27),
 ('CAPTAIN AMERICA IV/S', 27),
 ('STONECUTTER/UTAMA TO', 27),
 ('PISCES/NOAH PERRICON', 27),
 ('KINGSLEY, DANIEL', 27),
 ('BELASCO', 27),
 ('EDGERTON, MR.', 27),
 ('DEAN, DARBY', 27),
 ('AHURA', 27),
 ('SANTINI, DR. JOSE', 27),
 ('VAZHIN, ALEXEI MIKHA', 27),
 ('TEMPUS', 27),
 ('MARRS, DESMOND', 27),
 ('ADVERSARY', 27),
 ('JONES, DANIEL DANNY', 27),
 ('DRUID/DREDMUND CROMW', 27),
 ('DRAGONESS', 27),
 ('PATRICK, KERRY', 27),
 ('CAPTAIN HIP/FRED MAC', 26),
 ('SPEEDBALL II/DARRION', 26),
 ('TEMPEST II/NICOLETTE', 26),
 ('NEMESIS', 26),
 ('PYLLAR', 26),
 ('LODESTONE/ANDREA', 26),
 ('KOFI [KYMELLIAN]', 26),
 ('SERESTUS', 26),
 ('TARANTULA/ANTON MIGU', 26),
 ('FITZROY, TREVOR', 26),
 ('DEATHLOK/COLONEL LUT', 26),
 ('YIN, ANGELA', 26),
 ('PRISM', 26),
 ('BENTLEY, VICTORIA', 26),
 ('PUPPY', 26),
 ('INFERNO III/SAMANTHA', 26),
 ('RUNCITER, GAIL', 26),
 ('POPEYE', 26),
 ("CH'THON", 26),
 ('VOLLA', 26),
 ('MROTH', 26),
 ('PRIMUS', 26),
 ('TRAINER, DR. SEWARD', 26),
 ('PROJECTOR/', 26),
 ('WIPEOUT', 26),
 ('HESCAMAR/MARNOT [ASG', 26),
 ('MANTA', 26),
 ('BAILEY, SARA GREY', 26),
 ('SPOOR/ANDREW HAMISH ', 26),
 ('MIDAS', 26),
 ('LOCKLIN, BEN', 26),
 ('DOOMSDAY MAN', 26),
 ('BEDLAM II/OLISA KABA', 26),
 ('PEEL, EMMA', 26),
 ('STEED, JOHN', 26),
 ('DOMINA', 26),
 ('PINK PEARL/', 26),
 ('QUAGMIRE', 26),
 ('DURAN, CONAL', 26),
 ('BONEBREAKER/', 26),
 ('JAYE-FIRESTONE, GING', 26),
 ('TRANTRA', 26),
 ('MOREAU, DAVID', 26),
 ('REE', 26),
 ('JAHF', 26),
 ('GOLDDIGGER', 25),
 ('ANGLER', 25),
 ('BLUE STREAK/', 25),
 ('STACY, PAUL', 25),
 ('GRANT, SILAS', 25),
 ('CYCLONE', 25),
 ('RED WOLF/WILDRUN', 25),
 ('KRAMER, KENNY', 25),
 ('BLACK FOX II/', 25),
 ('TICKTOCK', 25),
 ('ARIES/MARCUS LASSITE', 25),
 ('ULTIMO', 25),
 ('AUGUST PERSONAGE IN ', 25),
 ('ENFORCER/DELAZNY', 25),
 ('BENEDICT', 25),
 ('EVANS, DORIS', 25),
 ('SPYMASTER II', 25),
 ("MIDNIGHT/M'NAI", 25),
 ('BANKS, SHARON', 25),
 ('MAGIC', 25),
 ('HINDSIGHT LAD/CARLTO', 25),
 ('AQUARIUS/DARREN BENT', 25),
 ('SILVER SCORPION/ELIS', 25),
 ('BUZZ', 25),
 ('AVIUS', 25),
 ('PHANTOM RIDER II/HAM', 25),
 ('KWAN, TERRY', 25),
 ('RAX', 25),
 ('LEWIS, DEVON', 25),
 ('DEATH-STALKER', 25),
 ('CAT MAN/HORGAN', 25),
 ('ZELDA', 25),
 ('MACLAIN, DR. MYRON', 25),
 ('FRANKENSTEIN MONSTER', 25),
 ('BIRD MAN/HENRY HAWK', 25),
 ('DESTRUCTOR/PROFESSOR', 25),
 ('CYBELE [ETERNAL]', 25),
 ('STANE, OBADIAH', 25),
 ('DELGADO, FATHER', 25),
 ('GREEN GOBLIN V/', 25),
 ("D'ANGELO, LIEUTENANT", 25),
 ('FATALE/PAMELA GREENW', 25),
 ('SUNSTROKE/', 24),
 ('SMOKESCREEN/', 24),
 ('ACBA', 24),
 ('FIREFIGHT/', 24),
 ("O'NEIL, BRIDGET", 24),
 ('UROC', 24),
 ('BARON BLOOD II/VICTO', 24),
 ('KAMIKAZE', 24),
 ('CARPENTER, LARRY', 24),
 ('ROCKETLAUNCHER/', 24),
 ('FORTUNATO', 24),
 ('MARRERO, ED', 24),
 ('BLOODLUST', 24),
 ('FROG MAN/FROG LE BLA', 24),
 ('STATIC', 24),
 ('BLACKHEART', 24),
 ('ICEMASTER/', 24),
 ('ERG', 24),
 ('DR. OCTOPUS II/CAROL', 24),
 ('BAXTER, NOAH', 24),
 ('ANDERSON, CHIEF MAGI', 24),
 ('MOLTEN MAN/MARK RAXT', 24),
 ('AGINAR', 24),
 ('MAELEN', 24),
 ('SATURNYNE, OPAL LUNA', 24),
 ('SILVANI, VITTORIO', 24),
 ('PRETTY BOY', 24),
 ('DONALDSON, LINDA', 24),
 ('KALE, JENNIFER', 24),
 ('TATTERDEMALION/', 24),
 ('DESIGNATE/TARENE', 24),
 ('STANCHEK, DR.', 24),
 ('HORUS [EGYPTIAN GOD]', 24),
 ('AIRBORNE/', 24),
 ('DAMIAN, MARGO', 24),
 ('FAIRMONT, AMANDA MAN', 24),
 ('HELLEN', 24),
 ('THUNDERBIRD/JOHN PRO', 24),
 ('BOOBYTRAP/', 24),
 ('SLAB/KRIS ANDERSON', 24),
 ('APE/', 24),
 ('EGMA', 24),
 ('LOBO, EDUARDO', 24),
 ('PLAGUE', 24),
 ('KAFKA, DR. ASHLEY', 24),
 ('PRODIGAL/', 24),
 ('SOPHIE', 24),
 ('SUPERCHARGER/', 24),
 ('RAVEN, LT. JACOB', 24),
 ('MAN-MOUNTAIN MARKO', 24),
 ('EDDA', 24),
 ("WILL O' THE WISP/JAC", 24),
 ('PERRIKUS', 24),
 ('DADDY LONGLEGS/RAMSE', 24),
 ('CALDRONE, JAVIER', 24),
 ('NEFFETHESK', 24),
 ('MIDGET', 24),
 ('WORM', 24),
 ('WILFORD, BONNIE', 24),
 ('MICROMAX/SCOTT WRIGH', 24),
 ('TESS-ONE', 24),
 ('GUARDIAN V/JAMES MAC', 24),
 ('TWAKI, DR. TED', 24),
 ('CONAN, NEAL', 24),
 ('RIDER, CHARLES', 24),
 ('KNOCKOUT', 23),
 ('MANTIS (SPACE PHANTO', 23),
 ('ARACHNE', 23),
 ('URANIUM', 23),
 ('SHADOWCAT 2013', 23),
 ('HEALER', 23),
 ('GALEN KOR, ADMIRAL [', 23),
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 ('ASTRONOMER', 23),
 ('GEIRRODUR', 23),
 ('CERA, KAY', 23),
 ('SPYMASTER/JAKE JORDA', 23),
 ('RADIUS/JARED CORBO', 23),
 ('CALIFORNIUM', 23),
 ('MAYHEM/DET. BRIGID O', 23),
 ('ANTRO', 23),
 ('PUNISHER', 23),
 ('DARKOTH/MAJOR DESMON', 23),
 ('SILVER II', 23),
 ('ANELLE', 23),
 ('MINDBLAST', 23),
 ('KHANDRUVITCH, DR. VA', 23),
 ('BERYLLIUM', 23),
 ('KANGAROO II/', 23),
 ('TOWER', 23),
 ('TRASK, LAWRENCE LARR', 23),
 ('CHESNEY, SANFORD', 23),
 ('PLUTONIUM', 23),
 ('BEETLE II/LEILA DAVI', 23),
 ('FLEX/ADRIAN CORBO', 23),
 ('ALCHEMY/THOMAS JONES', 23),
 ('MR. JIP', 23),
 ('BUSHWACKER/CARL', 23),
 ('BOUDREAUX, BELLA DON', 23),
 ('ELSIE DEE', 23),
 ('HALE, JONAS', 23),
 ('GOLD', 23),
 ('LEVIATHAN II/EDWARD ', 23),
 ('DIGGER', 23),
 ('HOCHBERG, ROGER', 23),
 ('KUKULCAN/VIRAKO/QUET', 23),
 ('JONES, NELL', 23),
 ('RADIUM', 23),
 ('RED RAVEN II', 23),
 ('FORTUNATO, GIACOMO J', 23),
 ('NEPTUNIUM', 23),
 ('CESIUM', 23),
 ('BADALAMENTE, JESUS', 23),
 ('CHANDLER, PEGGY', 23),
 ('EUROPIUM', 23),
 ('TRADER', 23),
 ('BLACK TALON II/SAMUE', 23),
 ('TEMPLE, CLAIRE', 23),
 ('SUNSHINE/AUTUMN MACR', 23),
 ('ALBERT', 23),
 ('WARTON/WARBECK', 23),
 ('KRISTA', 23),
 ('CHANCE II/NICHOLAS P', 23),
 ('MURMUR II/ARLETTE TR', 23),
 ('ULTIMATOR/', 23),
 ('TANAKA, OPAL', 22),
 ('RANDOM II/MARSHALL E', 22),
 ('CLARK, JOHN', 22),
 ('DRAKE, CARLTON', 22),
 ('TOPSPIN/DARREN MITCH', 22),
 ('NOCENTI, ANN', 22),
 ('STOCKBRIDGE, CHARLES', 22),
 ('SOFEN, MARION', 22),
 ('ANNALEE', 22),
 ('MAINFRAME II', 22),
 ('SHARD', 22),
 ('MOUNTJOY', 22),
 ('THUNDERSWORD', 22),
 ('FORCE/CLAYTON WILSON', 22),
 ('THORSON, DR. WALTER', 22),
 ('NEEDLE', 22),
 ('CAREGIVER/RUBANNA LA', 22),
 ('WOLVERINE 2013', 22),
 ('STUNNER/ANGELINA BRA', 22),
 ('KING, HANNIBAL/HENRY', 22),
 ('MASTER MAN/WILHELM L', 22),
 ('FURGAR', 22),
 ('COLOSSUS 2013', 22),
 ('LUMPKIN, WILHEMINA B', 22),
 ('GOLDFIRE/AMEIKO SABU', 22),
 ('LOCKE, DR. ALEXANDER', 22),
 ('PAGAN', 22),
 ('RIDER, GLORIA', 22),
 ('SOFEN, KARL AUGUST', 22),
 ('GROVEL', 22),
 ('OVERRIDE/DR. GREGORY', 22),
 ('LEI KUNG, THE THUNDE', 22),
 ('TARGON', 22),
 ('JAKAR', 22),
 ('FERRIS', 22),
 ('SAROYAN, NEAL', 22),
 ('EVERY-MAN', 22),
 ('EQUILIBRIUS', 22),
 ('MACHETE/FERDINAND LO', 22),
 ('PIROUETTE/JOBETH', 22),
 ('MODT', 22),
 ('EL JAGUAR', 22),
 ('SAPPHIRAS', 22),
 ('NIGHT RIDER', 22),
 ('GILBERT, ROXANNE', 22),
 ('LIGHTMASTER/EDWARD L', 22),
 ('FANG II', 22),
 ('ARIZE', 22),
 ('NEVILLE, KATE', 22),
 ('DEMONICUS/DR. DOUGLA', 22),
 ('CYNAS', 22),
 ('MACINTOSH, ANN', 22),
 ('CLARK, SARAH', 22),
 ('STINGER III/', 22),
 ('SENSATIONAL HYDRA/A ', 22),
 ('TURK', 22),
 ('STOCKBRIDGE, DEANNA', 22),
 ('OMEGA-20/EUGENE', 21),
 ('MCPHERSON, LING', 21),
 ('BARIUM', 21),
 ('YRDISIS', 21),
 ('PUNCHOUT', 21),
 ('JOHNSON, ANNA', 21),
 ('ERBIUM', 21),
 ('SODIUM', 21),
 ('REPTILLA', 21),
 ('INDIUM', 21),
 ('ION/', 21),
 ('SCANDIUM', 21),
 ...]

Using Supervised Learning

While using the above method is simple for recommending friends, it is not accurate enough. For example, we may not recommend that a user connects to other people with similar interests or to users that are at a distance greater than 2. To overcome this issue, we can create a machine learning classifier that can predict the probability of a link existing. There are two scenarios for which this type of classifier can be useful:

  • predicting missing links.
  • predicting future links.

To predict links, we first need to construct a dataset of positive and negative examples. We select the positive links as observed links in the network, while negative links are selected at random (for a better strategy for the selection of negative links please see Fire et al., 2013). Let's write a function that creates negative and positive link sets:

In [7]:
# This is for predicting missing links scenario
import random 

def select_random_existing_links(g, n):
    l =list(g.edges())
    random.shuffle(l)
    return l[:n]

def select_random_non_existing_links(g, n):
    select_links = set()
    v_list = list(g.nodes())
    while len(select_links) < n:
        u = random.choice(v_list)
        v = random.choice(v_list)
        if g.has_edge(u,v) or (u,v) in select_links:
            continue
        else:
            select_links.add((u,v))
    return list(select_links)
            
    
postive_links = select_random_existing_links(g,10000)
negative_links = select_random_non_existing_links(g,10000)

After creating negative and positive links, the next setup is to calculate the features of each link. Let’s create a function that for each link calculates several simple links features:

Note: For the next example, we are using the tqdm pacakge, which can be installed using:

In [8]:
!pip install tqdm
Requirement already satisfied: tqdm in /anaconda3/envs/massivedata/lib/python3.6/site-packages (4.36.1)
In [9]:
from tqdm import tqdm_notebook as tqdm 
def friends_measure(g, u,v):
    s = 0
    for x in g.neighbors(u):
        for y in g.neighbors(v):
            if g.has_edge(x,y) or x == y:
                s += 1
    return s

def total_friends(g, u, v):   
    return len(set(g.neighbors(v)) | set(g.neighbors(u)))

def get_features_dict(g,u, v):
    removed_edge = False
    if g.has_edge(u,v):
        g.remove_edge(u,v) #  remove edge before feature calculation
        removed_edge = True
    try:
        d = {
            'degree_v': len(list(g.neighbors(v))),
            'degree_u': len(list(g.neighbors(u))),
            'total_friends': total_friends(g,u,v),
            'common_friends': len(common_friends(g,u,v)),
            'jaccard_coefficient': list(nx.jaccard_coefficient(g,[(u,v)]))[0][2],
            'adamic_adar_index': list(nx.adamic_adar_index(g,[(u,v)]))[0][2],
            'friends_measure': friends_measure(g, u,v)
        }
    except:
        if removed_edge:
            g.add_edge(u,v) # return the edge back to the graph
        return None

    
    if removed_edge:
        g.add_edge(u,v) # return the edge back to the graph
    return d

links_features = []

for i in tqdm(range(len(postive_links))):    
    u,v = postive_links[i]
    d = get_features_dict(g, u,v )
    if d is None:
        continue
    d['class'] = True
    links_features.append(d)

for i in tqdm(range(len(negative_links))):    
    u,v = negative_links[i]
    d = get_features_dict(g, u,v )
    if d is None:
        continue
    d['class'] = False
    links_features.append(d)
links_features[0]


Out[9]:
{'degree_v': 126,
 'degree_u': 595,
 'total_friends': 665,
 'common_friends': 56,
 'jaccard_coefficient': 0.08421052631578947,
 'adamic_adar_index': 9.63012536981076,
 'friends_measure': 15178,
 'class': True}

Let's load the dictionary into an SFrame object:

In [10]:
import turicreate as tc
features_list = ['degree_v', 'degree_u', 'total_friends', 'common_friends', 'jaccard_coefficient', 'adamic_adar_index', 'friends_measure', 'class' ]
data_dict = {f:[] for f in features_list}
 

for d in links_features:
    for feature in features_list:
        data_dict[feature].append(d[feature])
sf = tc.SFrame(data_dict)
sf
    
Out[10]:
adamic_adar_index class common_friends degree_u degree_v friends_measure jaccard_coefficient total_friends
9.63012536981076 1 56 595 126 15178 0.08421052631578947 665
4.186902067216953 1 24 856 24 8355 0.028037383177570093 856
4.242886491926066 1 23 1235 34 8206 0.018459069020866775 1246
4.738654289778933 1 21 96 21 859 0.21875 96
6.341018866909226 1 33 47 76 2158 0.36666666666666664 90
5.563543248815053 1 31 438 41 6187 0.06919642857142858 448
3.6362877162031806 1 15 966 21 1538 0.015432098765432098 972
12.239509304670118 1 72 476 86 16127 0.1469387755102041 490
34.135022263064855 1 192 1081 376 54647 0.15177865612648223 1265
15.612959228629327 1 72 642 87 10550 0.1095890410958904 657
[20000 rows x 8 columns]
Note: Only the head of the SFrame is printed.
You can use print_rows(num_rows=m, num_columns=n) to print more rows and columns.

Let's use the data to construct a classifier:

In [11]:
test, train  = sf.random_split(0.2)
print(f"Train size {len(train)}\nTest size: {len(test)}" )
features_list = ['degree_v', 'degree_u', 'total_friends', 'common_friends', 'jaccard_coefficient', 'adamic_adar_index', 'friends_measure' ]
cls = tc.classifier.create(train,features=features_list, target="class")
Train size 16031
Test size: 3969
PROGRESS: Creating a validation set from 5 percent of training data. This may take a while.
          You can set ``validation_set=None`` to disable validation tracking.

PROGRESS: The following methods are available for this type of problem.
PROGRESS: BoostedTreesClassifier, RandomForestClassifier, DecisionTreeClassifier, SVMClassifier, LogisticClassifier
PROGRESS: The returned model will be chosen according to validation accuracy.
Boosted trees classifier:
--------------------------------------------------------
Number of examples          : 15229
Number of classes           : 2
Number of feature columns   : 7
Number of unpacked features : 7
+-----------+--------------+-------------------+---------------------+-------------------+---------------------+
| Iteration | Elapsed Time | Training Accuracy | Validation Accuracy | Training Log Loss | Validation Log Loss |
+-----------+--------------+-------------------+---------------------+-------------------+---------------------+
| 1         | 0.021860     | 0.982402          | 0.973815            | 0.454018          | 0.458446            |
| 2         | 0.031141     | 0.982730          | 0.971322            | 0.320561          | 0.326452            |
| 3         | 0.041332     | 0.983978          | 0.976309            | 0.236801          | 0.244700            |
| 4         | 0.052336     | 0.984175          | 0.976309            | 0.180736          | 0.190014            |
| 5         | 0.061114     | 0.983912          | 0.976309            | 0.141353          | 0.153566            |
| 10        | 0.110484     | 0.986933          | 0.976309            | 0.059458          | 0.081363            |
+-----------+--------------+-------------------+---------------------+-------------------+---------------------+
Random forest classifier:
--------------------------------------------------------
Number of examples          : 15229
Number of classes           : 2
Number of feature columns   : 7
Number of unpacked features : 7
+-----------+--------------+-------------------+---------------------+-------------------+---------------------+
| Iteration | Elapsed Time | Training Accuracy | Validation Accuracy | Training Log Loss | Validation Log Loss |
+-----------+--------------+-------------------+---------------------+-------------------+---------------------+
| 1         | 0.018019     | 0.974785          | 0.961347            | 0.178501          | 0.198648            |
| 2         | 0.027120     | 0.979316          | 0.972569            | 0.168392          | 0.181552            |
| 3         | 0.036311     | 0.981220          | 0.973815            | 0.167023          | 0.180029            |
| 4         | 0.046117     | 0.981483          | 0.971322            | 0.166206          | 0.178449            |
| 5         | 0.054712     | 0.982139          | 0.972569            | 0.165200          | 0.177956            |
| 10        | 0.099185     | 0.981942          | 0.971322            | 0.165173          | 0.177849            |
+-----------+--------------+-------------------+---------------------+-------------------+---------------------+
Decision tree classifier:
--------------------------------------------------------
Number of examples          : 15229
Number of classes           : 2
Number of feature columns   : 7
Number of unpacked features : 7
+-----------+--------------+-------------------+---------------------+-------------------+---------------------+
| Iteration | Elapsed Time | Training Accuracy | Validation Accuracy | Training Log Loss | Validation Log Loss |
+-----------+--------------+-------------------+---------------------+-------------------+---------------------+
| 1         | 0.020862     | 0.982402          | 0.973815            | 0.164380          | 0.178365            |
+-----------+--------------+-------------------+---------------------+-------------------+---------------------+
SVM:
--------------------------------------------------------
Number of examples          : 15229
Number of classes           : 2
Number of feature columns   : 7
Number of unpacked features : 7
Number of coefficients    : 8
Starting L-BFGS
--------------------------------------------------------
+-----------+----------+-----------+--------------+-------------------+---------------------+
| Iteration | Passes   | Step size | Elapsed Time | Training Accuracy | Validation Accuracy |
+-----------+----------+-----------+--------------+-------------------+---------------------+
| 0         | 2        | 1.000000  | 0.043674     | 0.499770          | 0.497506            |
| 1         | 5        | 5.000000  | 0.123181     | 0.926981          | 0.920200            |
| 2         | 6        | 5.000000  | 0.164217     | 0.919102          | 0.903990            |
| 3         | 8        | 2.139840  | 0.222925     | 0.953050          | 0.947631            |
| 4         | 10       | 2.139840  | 0.286101     | 0.956990          | 0.956359            |
| 9         | 16       | 1.490248  | 0.512787     | 0.968547          | 0.963840            |
+-----------+----------+-----------+--------------+-------------------+---------------------+
Logistic regression:
--------------------------------------------------------
Number of examples          : 15229
Number of classes           : 2
Number of feature columns   : 7
Number of unpacked features : 7
Number of coefficients      : 8
Starting Newton Method
--------------------------------------------------------
+-----------+----------+--------------+-------------------+---------------------+
| Iteration | Passes   | Elapsed Time | Training Accuracy | Validation Accuracy |
+-----------+----------+--------------+-------------------+---------------------+
| 1         | 2        | 0.043056     | 0.911419          | 0.900249            |
| 2         | 3        | 0.088976     | 0.939064          | 0.936409            |
| 3         | 4        | 0.134020     | 0.957647          | 0.950125            |
| 4         | 5        | 0.173059     | 0.967431          | 0.962594            |
| 5         | 6        | 0.215573     | 0.973800          | 0.966334            |
| 6         | 7        | 0.258268     | 0.975967          | 0.975062            |
+-----------+----------+--------------+-------------------+---------------------+
TERMINATED: Terminated due to numerical difficulties.
This model may not be ideal. To improve it, consider doing one of the following:
(a) Increasing the regularization.
(b) Standardizing the input data.
(c) Removing highly correlated features.
(d) Removing `inf` and `NaN` values in the training data.
PROGRESS: Model selection based on validation accuracy:
PROGRESS: ---------------------------------------------
PROGRESS: BoostedTreesClassifier          : 0.9763092269326683
PROGRESS: RandomForestClassifier          : 0.9713216957605985
PROGRESS: DecisionTreeClassifier          : 0.972568578553616
PROGRESS: SVMClassifier                   : 0.9638403990024937
PROGRESS: LogisticClassifier              : 0.9750623441396509
PROGRESS: ---------------------------------------------
PROGRESS: Selecting BoostedTreesClassifier based on validation set performance.

We can also calculate the classifier's AUC:

In [12]:
cls
Out[12]:
Class                          : BoostedTreesClassifier

Schema
------
Number of examples             : 15229
Number of feature columns      : 7
Number of unpacked features    : 7
Number of classes              : 2

Settings
--------
Number of trees                : 10
Max tree depth                 : 6
Training time (sec)            : 0.1109
Training accuracy              : 0.9869
Validation accuracy            : 0.9763
Training log_loss              : 0.0595
Validation log_loss            : 0.0814
Training auc                   : 0.9989
Validation auc                 : 0.9945
In [13]:
results = cls.evaluate(test)
results
Out[13]:
{'accuracy': 0.9778281683043588,
 'auc': 0.9960666140200752,
 'confusion_matrix': Columns:
 	target_label	int
 	predicted_label	int
 	count	int
 
 Rows: 4
 
 Data:
 +--------------+-----------------+-------+
 | target_label | predicted_label | count |
 +--------------+-----------------+-------+
 |      1       |        0        |   42  |
 |      1       |        1        |  1948 |
 |      0       |        1        |   46  |
 |      0       |        0        |  1933 |
 +--------------+-----------------+-------+
 [4 rows x 3 columns],
 'f1_score': 0.9779116465863453,
 'log_loss': 0.07745466638213759,
 'precision': 0.9769307923771314,
 'recall': 0.978894472361809,
 'roc_curve': Columns:
 	threshold	float
 	fpr	float
 	tpr	float
 	p	int
 	n	int
 
 Rows: 100001
 
 Data:
 +-----------+-----+-----+------+------+
 | threshold | fpr | tpr |  p   |  n   |
 +-----------+-----+-----+------+------+
 |    0.0    | 1.0 | 1.0 | 1990 | 1979 |
 |   1e-05   | 1.0 | 1.0 | 1990 | 1979 |
 |   2e-05   | 1.0 | 1.0 | 1990 | 1979 |
 |   3e-05   | 1.0 | 1.0 | 1990 | 1979 |
 |   4e-05   | 1.0 | 1.0 | 1990 | 1979 |
 |   5e-05   | 1.0 | 1.0 | 1990 | 1979 |
 |   6e-05   | 1.0 | 1.0 | 1990 | 1979 |
 |   7e-05   | 1.0 | 1.0 | 1990 | 1979 |
 |   8e-05   | 1.0 | 1.0 | 1990 | 1979 |
 |   9e-05   | 1.0 | 1.0 | 1990 | 1979 |
 +-----------+-----+-----+------+------+
 [100001 rows x 5 columns]
 Note: Only the head of the SFrame is printed.
 You can use print_rows(num_rows=m, num_columns=n) to print more rows and columns.}
In [14]:
import matplotlib.pyplot as plt
%matplotlib inline

plt.style.use('ggplot')
plt.plot(results['roc_curve']['fpr'],
         results['roc_curve']['tpr'],
         label='ROC Curve (AUC = %0.5f)' %results['auc'])

plt.xlim([-0.02, 1.0])
plt.ylim([0., 1.02])
plt.xlabel('FPR')
plt.ylabel('TPR')
plt.title('ROC curve')
plt.legend()
plt.show()

We can check the classifier's prediction probabilities:

In [15]:
cls.classify(test)
Out[15]:
class probability
1 0.9751444458961487
1 0.9751444458961487
0 0.6578031778335571
1 0.9754379391670227
1 0.9348450899124146
1 0.9716320037841797
1 0.9716320037841797
0 0.6157830059528351
1 0.9754379391670227
1 0.8576820492744446
[3969 rows x 2 columns]
Note: Only the head of the SFrame is printed.
You can use print_rows(num_rows=m, num_columns=n) to print more rows and columns.

Let's check how well, we can predict links by only using the common-friends:

In [16]:
cls = tc.classifier.create(train,features=["common_friends"], target="class")
results = cls.evaluate(test)
results
PROGRESS: Creating a validation set from 5 percent of training data. This may take a while.
          You can set ``validation_set=None`` to disable validation tracking.

PROGRESS: The following methods are available for this type of problem.
PROGRESS: BoostedTreesClassifier, RandomForestClassifier, DecisionTreeClassifier, SVMClassifier, LogisticClassifier
PROGRESS: The returned model will be chosen according to validation accuracy.
Boosted trees classifier:
--------------------------------------------------------
Number of examples          : 15229
Number of classes           : 2
Number of feature columns   : 1
Number of unpacked features : 1
+-----------+--------------+-------------------+---------------------+-------------------+---------------------+
| Iteration | Elapsed Time | Training Accuracy | Validation Accuracy | Training Log Loss | Validation Log Loss |
+-----------+--------------+-------------------+---------------------+-------------------+---------------------+
| 1         | 0.011448     | 0.943069          | 0.958853            | 0.483386          | 0.475059            |
| 2         | 0.037764     | 0.943069          | 0.958853            | 0.368681          | 0.354964            |
| 3         | 0.052362     | 0.943069          | 0.958853            | 0.298004          | 0.280471            |
| 4         | 0.059595     | 0.943069          | 0.958853            | 0.252069          | 0.231606            |
| 5         | 0.067634     | 0.943069          | 0.958853            | 0.221329          | 0.198702            |
| 10        | 0.104377     | 0.943069          | 0.958853            | 0.164212          | 0.136824            |
+-----------+--------------+-------------------+---------------------+-------------------+---------------------+
Random forest classifier:
--------------------------------------------------------
Number of examples          : 15229
Number of classes           : 2
Number of feature columns   : 1
Number of unpacked features : 1
+-----------+--------------+-------------------+---------------------+-------------------+---------------------+
| Iteration | Elapsed Time | Training Accuracy | Validation Accuracy | Training Log Loss | Validation Log Loss |
+-----------+--------------+-------------------+---------------------+-------------------+---------------------+
| 1         | 0.010177     | 0.943069          | 0.958853            | 0.232676          | 0.209531            |
| 2         | 0.017015     | 0.943069          | 0.958853            | 0.232597          | 0.209418            |
| 3         | 0.024316     | 0.943069          | 0.958853            | 0.232495          | 0.209478            |
| 4         | 0.031075     | 0.943069          | 0.958853            | 0.232571          | 0.209626            |
| 5         | 0.037741     | 0.943069          | 0.958853            | 0.232553          | 0.209676            |
| 10        | 0.074133     | 0.943069          | 0.958853            | 0.232621          | 0.209785            |
+-----------+--------------+-------------------+---------------------+-------------------+---------------------+
Decision tree classifier:
--------------------------------------------------------
Number of examples          : 15229
Number of classes           : 2
Number of feature columns   : 1
Number of unpacked features : 1
+-----------+--------------+-------------------+---------------------+-------------------+---------------------+
| Iteration | Elapsed Time | Training Accuracy | Validation Accuracy | Training Log Loss | Validation Log Loss |
+-----------+--------------+-------------------+---------------------+-------------------+---------------------+
| 1         | 0.010577     | 0.943069          | 0.958853            | 0.232643          | 0.209640            |
+-----------+--------------+-------------------+---------------------+-------------------+---------------------+
SVM:
--------------------------------------------------------
Number of examples          : 15229
Number of classes           : 2
Number of feature columns   : 1
Number of unpacked features : 1
Number of coefficients    : 2
Starting L-BFGS
--------------------------------------------------------
+-----------+----------+-----------+--------------+-------------------+---------------------+
| Iteration | Passes   | Step size | Elapsed Time | Training Accuracy | Validation Accuracy |
+-----------+----------+-----------+--------------+-------------------+---------------------+
| 0         | 3        | 5.000000  | 0.007891     | 0.500230          | 0.488778            |
| 1         | 6        | 2.500000  | 0.017523     | 0.752183          | 0.764339            |
| 2         | 11       | 1.245513  | 0.030740     | 0.821787          | 0.827930            |
| 3         | 12       | 1.245513  | 0.035083     | 0.893361          | 0.906484            |
| 4         | 14       | 1.245513  | 0.041269     | 0.928360          | 0.950125            |
| 9         | 21       | 1.245513  | 0.075452     | 0.939458          | 0.958853            |
+-----------+----------+-----------+--------------+-------------------+---------------------+
Logistic regression:
--------------------------------------------------------
Number of examples          : 15229
Number of classes           : 2
Number of feature columns   : 1
Number of unpacked features : 1
Number of coefficients      : 2
Starting Newton Method
--------------------------------------------------------
+-----------+----------+--------------+-------------------+---------------------+
| Iteration | Passes   | Elapsed Time | Training Accuracy | Validation Accuracy |
+-----------+----------+--------------+-------------------+---------------------+
| 1         | 2        | 0.018461     | 0.783374          | 0.799252            |
| 2         | 3        | 0.027386     | 0.837612          | 0.841646            |
| 3         | 4        | 0.035500     | 0.875566          | 0.890274            |
| 4         | 5        | 0.043887     | 0.909712          | 0.926434            |
| 5         | 6        | 0.052430     | 0.922057          | 0.941397            |
| 8         | 9        | 0.077325     | 0.928360          | 0.950125            |
+-----------+----------+--------------+-------------------+---------------------+
SUCCESS: Optimal solution found.

PROGRESS: Model selection based on validation accuracy:
PROGRESS: ---------------------------------------------
PROGRESS: BoostedTreesClassifier          : 0.9588528678304239
PROGRESS: RandomForestClassifier          : 0.9588528678304239
PROGRESS: DecisionTreeClassifier          : 0.9588528678304239
PROGRESS: SVMClassifier                   : 0.9588528678304239
PROGRESS: LogisticClassifier              : 0.9501246882793017
PROGRESS: ---------------------------------------------
PROGRESS: Selecting BoostedTreesClassifier based on validation set performance.
Out[16]:
{'accuracy': 0.9372637944066515,
 'auc': 0.9829088596088071,
 'confusion_matrix': Columns:
 	target_label	int
 	predicted_label	int
 	count	int
 
 Rows: 4
 
 Data:
 +--------------+-----------------+-------+
 | target_label | predicted_label | count |
 +--------------+-----------------+-------+
 |      1       |        0        |   69  |
 |      1       |        1        |  1921 |
 |      0       |        1        |  180  |
 |      0       |        0        |  1799 |
 +--------------+-----------------+-------+
 [4 rows x 3 columns],
 'f1_score': 0.9391346858958691,
 'log_loss': 0.17018960410565084,
 'precision': 0.91432651118515,
 'recall': 0.9653266331658291,
 'roc_curve': Columns:
 	threshold	float
 	fpr	float
 	tpr	float
 	p	int
 	n	int
 
 Rows: 100001
 
 Data:
 +-----------+-----+-----+------+------+
 | threshold | fpr | tpr |  p   |  n   |
 +-----------+-----+-----+------+------+
 |    0.0    | 1.0 | 1.0 | 1990 | 1979 |
 |   1e-05   | 1.0 | 1.0 | 1990 | 1979 |
 |   2e-05   | 1.0 | 1.0 | 1990 | 1979 |
 |   3e-05   | 1.0 | 1.0 | 1990 | 1979 |
 |   4e-05   | 1.0 | 1.0 | 1990 | 1979 |
 |   5e-05   | 1.0 | 1.0 | 1990 | 1979 |
 |   6e-05   | 1.0 | 1.0 | 1990 | 1979 |
 |   7e-05   | 1.0 | 1.0 | 1990 | 1979 |
 |   8e-05   | 1.0 | 1.0 | 1990 | 1979 |
 |   9e-05   | 1.0 | 1.0 | 1990 | 1979 |
 +-----------+-----+-----+------+------+
 [100001 rows x 5 columns]
 Note: Only the head of the SFrame is printed.
 You can use print_rows(num_rows=m, num_columns=n) to print more rows and columns.}

The above example is a "toy example" for creating a link prediction classifier. Even using the common-friends as a single feature can provide pretty good results.

Question: Why are the classification results so high?

Use the above classifier to create better friend recommendations to a specific vertex:

In [17]:
def get_friends_of_friends_sframe(g, v):
    l = find_friends_of_friends(g,v)
    features_list = ['degree_v', 'degree_u', 'total_friends', 'common_friends', 'jaccard_coefficient', 'adamic_adar_index', 'friends_measure', 'class' ]
    data_dict = {f:[] for f in features_list}
    data_dict['name'] = []
    for u in l:
        d = get_features_dict(g,u, v)
        d['class'] = g.has_edge(u,v)
        for f in features_list:
            data_dict[f].append(d[f])
        data_dict["name"].append(u)
    return tc.SFrame(data_dict)

r_sf = get_friends_of_friends_sframe(g, 'ANT-MAN/DR. HENRY J.')
r_sf
Out[17]:
adamic_adar_index class common_friends degree_u degree_v friends_measure jaccard_coefficient
0.8763205019834988 0 6 22 1082 4061 0.00546448087431694
3.8088215604162623 0 23 58 1082 7366 0.020590868397493287
1.7432602441427572 0 11 41 1082 4148 0.009892086330935251
0.29580751552438866 0 2 4 1082 749 0.0018450184501845018
0.14334147177031822 0 1 3 1082 605 0.0009225092250922509
0.16175772657433624 0 1 10 1082 224 0.0009165902841429881
1.0740765186349095 0 6 11 1082 1304 0.005519779208831647
1.3284494409303538 0 8 10 1082 1782 0.007380073800738007
0.4837108720332133 0 2 5 1082 693 0.0018433179723502304
1.1361420825493826 0 7 26 1082 3156 0.006357856494096276
name total_friends
WOLFE, MR. 1098
TANAKA, OPAL 1117
LOCUS 1112
KREIGKOPH, GEN. 1084
THREE-FACE 1084
CANE/ 1091
TOL-NOK 1087
HOBGOBLIN III/LEFTY 1084
COWAN, EARL 1085
CROTUS 1101
[5174 rows x 9 columns]
Note: Only the head of the SFrame is printed.
You can use print_rows(num_rows=m, num_columns=n) to print more rows and columns.
In [18]:
features_list = ['degree_v', 'degree_u', 'total_friends', 'common_friends', 'jaccard_coefficient', 'adamic_adar_index', 'friends_measure' ]
cls = tc.classifier.create(train,features=features_list, target="class")

r_sf['probability'] = cls.classify(r_sf[features_list])['probability']
r_sf = r_sf.sort(['probability','common_friends'] , ascending=False) 
r_sf['name', 'common_friends', 'probability'].print_rows(100)
r_sf['name', 'common_friends', 'probability'][5000:5100].print_rows(100)
PROGRESS: Creating a validation set from 5 percent of training data. This may take a while.
          You can set ``validation_set=None`` to disable validation tracking.

PROGRESS: The following methods are available for this type of problem.
PROGRESS: BoostedTreesClassifier, RandomForestClassifier, DecisionTreeClassifier, SVMClassifier, LogisticClassifier
PROGRESS: The returned model will be chosen according to validation accuracy.
Boosted trees classifier:
--------------------------------------------------------
Number of examples          : 15229
Number of classes           : 2
Number of feature columns   : 7
Number of unpacked features : 7
+-----------+--------------+-------------------+---------------------+-------------------+---------------------+
| Iteration | Elapsed Time | Training Accuracy | Validation Accuracy | Training Log Loss | Validation Log Loss |
+-----------+--------------+-------------------+---------------------+-------------------+---------------------+
| 1         | 0.016405     | 0.982271          | 0.980050            | 0.454090          | 0.456094            |
| 2         | 0.027891     | 0.983059          | 0.978803            | 0.321072          | 0.325265            |
| 3         | 0.037906     | 0.983453          | 0.978803            | 0.236729          | 0.243982            |
| 4         | 0.048596     | 0.984503          | 0.978803            | 0.180071          | 0.188782            |
| 5         | 0.058629     | 0.984963          | 0.980050            | 0.140834          | 0.151927            |
| 10        | 0.106595     | 0.987589          | 0.975062            | 0.058624          | 0.079087            |
+-----------+--------------+-------------------+---------------------+-------------------+---------------------+
Random forest classifier:
--------------------------------------------------------
Number of examples          : 15229
Number of classes           : 2
Number of feature columns   : 7
Number of unpacked features : 7
+-----------+--------------+-------------------+---------------------+-------------------+---------------------+
| Iteration | Elapsed Time | Training Accuracy | Validation Accuracy | Training Log Loss | Validation Log Loss |
+-----------+--------------+-------------------+---------------------+-------------------+---------------------+
| 1         | 0.019865     | 0.981023          | 0.975062            | 0.166870          | 0.175949            |
| 2         | 0.028210     | 0.981811          | 0.977556            | 0.164671          | 0.171538            |
| 3         | 0.038440     | 0.982468          | 0.973815            | 0.164590          | 0.176791            |
| 4         | 0.046585     | 0.983387          | 0.973815            | 0.164188          | 0.175833            |
| 5         | 0.056338     | 0.983124          | 0.970075            | 0.164509          | 0.178038            |
| 10        | 0.101126     | 0.982599          | 0.972569            | 0.165241          | 0.177215            |
+-----------+--------------+-------------------+---------------------+-------------------+---------------------+
Decision tree classifier:
--------------------------------------------------------
Number of examples          : 15229
Number of classes           : 2
Number of feature columns   : 7
Number of unpacked features : 7
+-----------+--------------+-------------------+---------------------+-------------------+---------------------+
| Iteration | Elapsed Time | Training Accuracy | Validation Accuracy | Training Log Loss | Validation Log Loss |
+-----------+--------------+-------------------+---------------------+-------------------+---------------------+
| 1         | 0.018705     | 0.982271          | 0.980050            | 0.164564          | 0.169696            |
+-----------+--------------+-------------------+---------------------+-------------------+---------------------+
SVM:
--------------------------------------------------------
Number of examples          : 15229
Number of classes           : 2
Number of feature columns   : 7
Number of unpacked features : 7
Number of coefficients    : 8
Starting L-BFGS
--------------------------------------------------------
+-----------+----------+-----------+--------------+-------------------+---------------------+
| Iteration | Passes   | Step size | Elapsed Time | Training Accuracy | Validation Accuracy |
+-----------+----------+-----------+--------------+-------------------+---------------------+
| 0         | 2        | 1.000000  | 0.041999     | 0.499376          | 0.504988            |
| 1         | 5        | 5.000000  | 0.120942     | 0.927244          | 0.930175            |
| 2         | 6        | 5.000000  | 0.160495     | 0.918642          | 0.917706            |
| 3         | 8        | 2.114265  | 0.220401     | 0.952787          | 0.950125            |
| 4         | 10       | 2.114265  | 0.280764     | 0.956793          | 0.953865            |
| 9         | 21       | 1.510830  | 0.595377     | 0.966183          | 0.965087            |
+-----------+----------+-----------+--------------+-------------------+---------------------+
Logistic regression:
--------------------------------------------------------
Number of examples          : 15229
Number of classes           : 2
Number of feature columns   : 7
Number of unpacked features : 7
Number of coefficients      : 8
Starting Newton Method
--------------------------------------------------------
+-----------+----------+--------------+-------------------+---------------------+
| Iteration | Passes   | Elapsed Time | Training Accuracy | Validation Accuracy |
+-----------+----------+--------------+-------------------+---------------------+
| 1         | 2        | 0.052639     | 0.910959          | 0.913965            |
| 2         | 3        | 0.095983     | 0.938538          | 0.940150            |
| 3         | 4        | 0.140870     | 0.957778          | 0.956359            |
| 4         | 5        | 0.185104     | 0.967168          | 0.967581            |
| 5         | 6        | 0.229164     | 0.973734          | 0.972569            |
| 6         | 7        | 0.272626     | 0.976033          | 0.976309            |
+-----------+----------+--------------+-------------------+---------------------+
TERMINATED: Terminated due to numerical difficulties.
This model may not be ideal. To improve it, consider doing one of the following:
(a) Increasing the regularization.
(b) Standardizing the input data.
(c) Removing highly correlated features.
(d) Removing `inf` and `NaN` values in the training data.
PROGRESS: Model selection based on validation accuracy:
PROGRESS: ---------------------------------------------
PROGRESS: BoostedTreesClassifier          : 0.9750623441396509
PROGRESS: RandomForestClassifier          : 0.972568578553616
PROGRESS: DecisionTreeClassifier          : 0.9788029925187033
PROGRESS: SVMClassifier                   : 0.9650872817955112
PROGRESS: LogisticClassifier              : 0.9763092269326683
PROGRESS: ---------------------------------------------
PROGRESS: Selecting DecisionTreeClassifier based on validation set performance.
+----------------------+----------------+-------------------+
|         name         | common_friends |    probability    |
+----------------------+----------------+-------------------+
|          T           |       4        | 0.879461832344532 |
|   MISS SACCHARINE    |       4        | 0.879461832344532 |
|    CARLTON, JAKE     |       4        | 0.879461832344532 |
| QUINN, MIRANDA/M'RYN |       4        | 0.879461832344532 |
|    MARTINEZ, MR.     |       4        | 0.879461832344532 |
|   MCCORMICK, BARRY   |       4        | 0.879461832344532 |
|     AUSTIN, GEN.     |       4        | 0.879461832344532 |
|   WILLIFORD, BRENT   |       4        | 0.879461832344532 |
|        COACH         |       4        | 0.879461832344532 |
|    GILMORE, BERT     |       4        | 0.879461832344532 |
|       CROSSBOW       |       4        | 0.879461832344532 |
|         DOOP         |       4        | 0.879461832344532 |
|    GALANNAN, BILL    |       4        | 0.879461832344532 |
|      CYCLOPS II      |       4        | 0.879461832344532 |
|    CARLTON, LUCY     |       4        | 0.879461832344532 |
|        TAKAR         |       4        | 0.879461832344532 |
|     KANE, SUGAR      |       4        | 0.879461832344532 |
|     WARP SAVANT      |       4        | 0.879461832344532 |
| CARRION III/DR. WILL |       4        | 0.879461832344532 |
|    KINCAID, MOSES    |       4        | 0.879461832344532 |
|   LONGBOW, DESIREE   |       4        | 0.879461832344532 |
| STAPHOS, DR. MONICA  |       4        | 0.879461832344532 |
|       REDJEK/        |       4        | 0.879461832344532 |
|    ROSETTA STONE     |       4        | 0.879461832344532 |
|      MINDSTORM/      |       4        | 0.879461832344532 |
|    FUSION/MARKLEY    |       4        | 0.879461832344532 |
|    SHERMAN, GEN.     |       4        | 0.879461832344532 |
|  DANTES, DR. EDWINA  |       4        | 0.879461832344532 |
|       HARMONY        |       4        | 0.879461832344532 |
|     MISTER CLEAN     |       4        | 0.879461832344532 |
|         BLOK         |       4        | 0.879461832344532 |
| TYPEFACE/GORDON THOM |       4        | 0.879461832344532 |
|        BARKER        |       4        | 0.879461832344532 |
|     EARLE, CARYN     |       4        | 0.879461832344532 |
|     TRI-SENTINEL     |       4        | 0.879461832344532 |
|      MAJOR LOVE      |       4        | 0.879461832344532 |
|      QUINN, MR.      |       4        | 0.879461832344532 |
|    PACE, MICHAEL     |       4        | 0.879461832344532 |
|   MARTINEZ, AKASHA   |       4        | 0.879461832344532 |
| PETRIE, JUDGE RICHAR |       4        | 0.879461832344532 |
|        CASPER        |       4        | 0.879461832344532 |
| FEIFFER, DEPUTY BEAU |       4        | 0.879461832344532 |
|       BOLERO/        |       4        | 0.879461832344532 |
|       LOCKDOWN       |       4        | 0.879461832344532 |
| SYNARIO/ANGELA BRADF |       4        | 0.879461832344532 |
|        WENDY         |       4        | 0.879461832344532 |
|        NUNTOO        |       4        | 0.879461832344532 |
|         YSSA         |       4        | 0.879461832344532 |
|     BRAINSTORM/      |       4        | 0.879461832344532 |
|          A           |       4        | 0.879461832344532 |
|    BURNING PUDDLE    |       4        | 0.879461832344532 |
| MCCULLOUGH, DR. ANDR |       4        | 0.879461832344532 |
|    HORTON, DOUGIE    |       4        | 0.879461832344532 |
|        TAREE         |       4        | 0.879461832344532 |
|      CANDLEMAS/      |       4        | 0.879461832344532 |
|    HOWLER II/ZED     |       4        | 0.879461832344532 |
| DE LA SALLE, BEATRIC |       4        | 0.879461832344532 |
|      ROSE III/       |       4        | 0.879461832344532 |
|    OBLITERATOR II    |       4        | 0.879461832344532 |
|   WILLIAMS, NILES    |       4        | 0.879461832344532 |
|    U-GO GIRL/EDIE    |       4        | 0.879461832344532 |
|        PAKAI         |       4        | 0.879461832344532 |
|        MR. X         |       4        | 0.879461832344532 |
|        SPOOKY        |       4        | 0.879461832344532 |
|      CHIMERA II      |       4        | 0.879461832344532 |
|         MACE         |       4        | 0.879461832344532 |
|     STEIN, EMIL      |       4        | 0.879461832344532 |
| ANARCHIST/TIKE ALICA |       4        | 0.879461832344532 |
|     MCNAIR, TAD      |       4        | 0.879461832344532 |
| THERMO/DR. WALTER MI |       4        | 0.879461832344532 |
| CONTRARES, ANASTASIA |       4        | 0.879461832344532 |
|        EXILE         |       4        | 0.879461832344532 |
|    MUNOZ, CARLOS     |       4        | 0.879461832344532 |
|  DARK COUNSEL/VARTU  |       3        | 0.879461832344532 |
|     YOUNG, AGNES     |       3        | 0.879461832344532 |
|       PHEMOUS        |       3        | 0.879461832344532 |
| ORPHAN II/GUY SMITH  |       3        | 0.879461832344532 |
|   MIRAGE III/MARGE   |       3        | 0.879461832344532 |
|       CYPRESS/       |       3        | 0.879461832344532 |
|   PATTERSON, JACK    |       3        | 0.879461832344532 |
|   ANDERSON, BAXTER   |       3        | 0.879461832344532 |
|      SOAP, DET.      |       3        | 0.879461832344532 |
|    SHANE, BRIDGIT    |       3        | 0.879461832344532 |
|   BRUKNER, ILANEY    |       3        | 0.879461832344532 |
| ROGERS, CAPT. STEVEN |       3        | 0.879461832344532 |
|       WALRUSS        |       3        | 0.879461832344532 |
|       ARCTURUS       |       3        | 0.879461832344532 |
|  CRIME-WAVE/HOLLIS   |       3        | 0.879461832344532 |
|    BIRD, TUESDAY     |       3        | 0.879461832344532 |
|        GRESIG        |       3        | 0.879461832344532 |
|   LANSING, CHERYL    |       3        | 0.879461832344532 |
|    POPPA WHEELER     |       3        | 0.879461832344532 |
|    POWERHOUSE II/    |       3        | 0.879461832344532 |
|        CHLOE         |       3        | 0.879461832344532 |
|         KHAT         |       3        | 0.879461832344532 |
|     COFFIN, RAY      |       3        | 0.879461832344532 |
| TYNE, ELIZABETH/JANI |       3        | 0.879461832344532 |
|    REVELATION II/    |       3        | 0.879461832344532 |
|       GLITTER/       |       3        | 0.879461832344532 |
|     CHEN, JOANNA     |       3        | 0.879461832344532 |
+----------------------+----------------+-------------------+
[5174 rows x 3 columns]

+----------------------+----------------+--------------------+
|         name         | common_friends |    probability     |
+----------------------+----------------+--------------------+
|       RAPUNZEL       |       28       | 0.5671592354774475 |
|      CHINA DOLL      |       28       | 0.5671592354774475 |
|         TORG         |       28       | 0.5671592354774475 |
|        FERRO2        |       28       | 0.5671592354774475 |
|  DAMIAN, DR. DANIEL  |       28       | 0.5671592354774475 |
|        YUKIO         |       28       | 0.5671592354774475 |
|        DIGGER        |       28       | 0.5671592354774475 |
|         THUG         |       28       | 0.5671592354774475 |
|       BODYBAG        |       28       | 0.5671592354774475 |
|       WAXWORK        |       28       | 0.5671592354774475 |
|  ROBERTSON, AMANDA   |       28       | 0.5671592354774475 |
|       TESS-ONE       |       28       | 0.5671592354774475 |
|     GATECRASHER      |       28       | 0.5671592354774475 |
|         DOMO         |       28       | 0.5671592354774475 |
|      SATANNISH       |       28       | 0.5671592354774475 |
| HATE-MONGER/ADOLF HI |       28       | 0.5671592354774475 |
|        DISCUS        |       28       | 0.5671592354774475 |
|     SCATTERBRAIN     |       28       | 0.5671592354774475 |
|   TATTERDEMALION/    |       28       | 0.5671592354774475 |
|  STYGIAN STARBENDER  |       28       | 0.5671592354774475 |
|       RINGTOSS       |       28       | 0.5671592354774475 |
|   SWANN, DR. EVAN    |       28       | 0.5671592354774475 |
|         YAP          |       28       | 0.5671592354774475 |
| VAZHIN, ALEXEI MIKHA |       28       | 0.5671592354774475 |
|        JOYBOY        |       28       | 0.5671592354774475 |
|    CASSADA, ROSA     |       28       | 0.5671592354774475 |
|        DOMINA        |       28       | 0.5671592354774475 |
|   GILBERT, ROXANNE   |       28       | 0.5671592354774475 |
|      PUDDLEGULP      |       28       | 0.5671592354774475 |
| NALES, DET. REBECCA  |       28       | 0.5671592354774475 |
| SPIDER-MAN III/MARTH |       27       | 0.5671592354774475 |
| LUNATIK/ARISEN TYRK  |       27       | 0.5671592354774475 |
|     BEETLE ROBOT     |       27       | 0.5671592354774475 |
|    FISK, VANESSA     |       27       | 0.5671592354774475 |
|        JEMIAH        |       27       | 0.5671592354774475 |
|      PRETTY BOY      |       27       | 0.5671592354774475 |
|         RAX          |       27       | 0.5671592354774475 |
|  BENNING, VERONICA   |       27       | 0.5671592354774475 |
|        SILVER        |       27       | 0.5671592354774475 |
| DORNOVA-BLONSKY, NAD |       27       | 0.5671592354774475 |
|       WIPEOUT        |       27       | 0.5671592354774475 |
|  WARD, SEN. STEWART  |       27       | 0.5671592354774475 |
|        CLOUD         |       27       | 0.5671592354774475 |
|        TEMPO         |       27       | 0.5671592354774475 |
|        PYLLAR        |       27       | 0.5671592354774475 |
| EXPEDITER II/ZOE CUL |       27       | 0.5671592354774475 |
|        MROTH         |       27       | 0.5671592354774475 |
| MACHETE/FERDINAND LO |       27       | 0.5671592354774475 |
|     MARTIN, TERI     |       27       | 0.5671592354774475 |
| THOMAS, COMMANDER DA |       27       | 0.5671592354774475 |
|   WILFORD, BONNIE    |       27       | 0.5671592354774475 |
|    MINISTER BLOOD    |       27       | 0.5671592354774475 |
|       NOBILUS        |       27       | 0.5671592354774475 |
|        PERUN         |       27       | 0.5671592354774475 |
| JAYE-FIRESTONE, GING |       27       | 0.5671592354774475 |
| HILDEGARDE [ASGARDIA |       27       | 0.5671592354774475 |
|      PRODIGAL/       |       27       | 0.5671592354774475 |
|     RED RAVEN II     |       26       | 0.5671592354774475 |
|    MIDNIGHT/M'NAI    |       26       | 0.5671592354774475 |
|    RUNCITER, GAIL    |       26       | 0.5671592354774475 |
|   BUSHWACKER/CARL    |       26       | 0.5671592354774475 |
|  BAILEY, SARA GREY   |       26       | 0.5671592354774475 |
|       IMPALA/        |       26       | 0.5671592354774475 |
|       NUMBERS        |       26       | 0.5671592354774475 |
|        PRISM         |       26       | 0.5671592354774475 |
|   AWESOME ANDROID    |       26       | 0.5671592354774475 |
|       SAMEDAR        |       26       | 0.5671592354774475 |
|      BLOODLUST       |       26       | 0.5671592354774475 |
|         GOG          |       26       | 0.5671592354774475 |
|    MOREAU, DAVID     |       26       | 0.5671592354774475 |
|        ADORA         |       26       | 0.5671592354774475 |
| HAROKIN [ASGARDIAN]  |       26       | 0.5671592354774475 |
|    CLEMSON, MIKE     |       26       | 0.5671592354774475 |
|      MALICE IV       |       26       | 0.5671592354774475 |
|        MAELEN        |       26       | 0.5671592354774475 |
|       KRUGGAR        |       26       | 0.5671592354774475 |
| WIZ KID/TAKESHI MATS |       26       | 0.5671592354774475 |
|       CH'THON        |       26       | 0.5671592354774475 |
| CAPTAIN AMERICA IV/S |       26       | 0.5671592354774475 |
|        NEEDLE        |       26       | 0.5671592354774475 |
| FATALE/PAMELA GREENW |       26       | 0.5671592354774475 |
|  BLESSING, MORGANA   |       26       | 0.5671592354774475 |
|      CERA, KAY       |       26       | 0.5671592354774475 |
|  DIAMONDHEAD/DYKER   |       26       | 0.5671592354774475 |
|       FEROCIA/       |       25       | 0.5671592354774475 |
|      MINDBLAST       |       25       | 0.5671592354774475 |
|    SHALLA BAL II     |       25       | 0.5671592354774475 |
| GENII/JASON KIMBALL  |       25       | 0.5671592354774475 |
|   DELGADO, FATHER    |       25       | 0.5671592354774475 |
|     JONES, BART      |       25       | 0.5671592354774475 |
|    HARVEST/CHI LO    |       25       | 0.5671592354774475 |
|       SERESTUS       |       25       | 0.5671592354774475 |
|        TOWER         |       25       | 0.5671592354774475 |
|    HIBBERT, HOPE     |       25       | 0.5671592354774475 |
|       KNOCKOUT       |       25       | 0.5671592354774475 |
|     HALE, JONAS      |       25       | 0.5671592354774475 |
| RANDOM II/MARSHALL E |       25       | 0.5671592354774475 |
|   PIROUETTE/JOBETH   |       25       | 0.5671592354774475 |
|      GREENSONG       |       25       | 0.5671592354774475 |
|  NEPTUNE [OLYMPIAN]  |       25       | 0.5671592354774475 |
+----------------------+----------------+--------------------+
[100 rows x 3 columns]

In [19]:
 r_sf.sort('common_friends', ascending=False)
Out[19]:
adamic_adar_index class common_friends degree_u degree_v friends_measure jaccard_coefficient
34.787538986069194 0 197 330 1082 55641 0.16213991769547326
32.49765233496148 0 184 521 1082 54425 0.12966878083157152
30.555924209079787 0 171 311 1082 46327 0.13993453355155483
30.111859635387702 0 171 334 1082 50290 0.13734939759036144
29.838643814460504 0 170 279 1082 50423 0.14273719563392107
29.188735513873617 0 168 300 1082 51439 0.13838550247116968
28.884310227841862 0 163 397 1082 49875 0.12386018237082067
27.31869529333024 0 160 304 1082 51232 0.13050570962479607
25.821185858193154 0 152 228 1082 48346 0.13126079447322972
25.882310796918194 0 152 244 1082 49490 0.12947189097103917
name total_friends probability
KA-ZAR/KEVIN PLUNDER 1215 0.8784257173538208
SUNSPOT/ROBERTO DACO 1419 0.8784257173538208
RHINO 1222 0.8784257173538208
HIGH EVOLUTIONARY/HE 1245 0.8784257173538208
SHANNA/SHANNA O'HARA 1191 0.8784257173538208
PALADIN/PAUL DENNIS 1214 0.8784257173538208
PHOENIX III/RACHEL S 1316 0.8784257173538208
GUARDIAN/JAMES MACDO 1226 0.8784257173538208
SABRA/RUTH BAT-SERAP 1158 0.8784257173538208
SNOWBIRD/NARYA/ANNE 1174 0.8784257173538208
[5174 rows x 10 columns]
Note: Only the head of the SFrame is printed.
You can use print_rows(num_rows=m, num_columns=n) to print more rows and columns.

We can take a different approach to build the classifier by selecting a different set of negative links that are not selected completely in random (this selection may be very important in massive graphs - why?):

In [20]:
def get_random_friend_of_friend(g, v, max_tries=100):
    for i in range(max_tries):
        u = random.choice(list(g.neighbors(v)))
        x = random.choice(list(g.neighbors(u)))
        if not g.has_edge(v,x):
            return x
    return None
    
def select_random_non_existing_links_distance_2(g, n):
    select_links = set()
    v_list = list(g.nodes())
    while len(select_links) < n:
        v = random.choice(v_list)
        u = get_random_friend_of_friend(g, v)
        if u is None:
            continue
        select_links.add((v,u))
    return list(select_links)

negative_links = select_random_non_existing_links_distance_2(g, 10000)
len(negative_links)
Out[20]:
10000

Let's retrain the classifier with the new example of negative links:

In [21]:
links_features = []
for i in tqdm(range(len(postive_links))):    
    u,v = postive_links[i]
    d = get_features_dict(g, u,v )
    d['class'] = True
    links_features.append(d)

for i in tqdm(range(len(negative_links))):    
    u,v = negative_links[i]
    d = get_features_dict(g, u,v )
    if d is None:
        continue
    d['class'] = False
    links_features.append(d)

    
features_list = ['degree_v', 'degree_u', 'total_friends', 'common_friends', 'jaccard_coefficient', 'adamic_adar_index', 'friends_measure', 'class' ]
data_dict = {f:[] for f in features_list}
for d in links_features:
    for feature in features_list:
        data_dict[feature].append(d[feature])
sf = tc.SFrame(data_dict)
sf


Out[21]:
adamic_adar_index class common_friends degree_u degree_v friends_measure jaccard_coefficient total_friends
9.630125369810763 1 56 595 126 15178 0.08421052631578947 665
4.186902067216954 1 24 856 24 8355 0.028037383177570093 856
4.242886491926065 1 23 1235 34 8206 0.018459069020866775 1246
4.738654289778934 1 21 96 21 859 0.21875 96
6.341018866909225 1 33 47 76 2158 0.36666666666666664 90
5.563543248815053 1 31 438 41 6187 0.06919642857142858 448
3.6362877162031806 1 15 966 21 1538 0.015432098765432098 972
12.239509304670118 1 72 476 86 16127 0.1469387755102041 490
34.13502226306484 1 192 1081 376 54647 0.15177865612648223 1265
15.612959228629332 1 72 642 87 10550 0.1095890410958904 657
[19995 rows x 8 columns]
Note: Only the head of the SFrame is printed.
You can use print_rows(num_rows=m, num_columns=n) to print more rows and columns.
In [22]:
test, train  = sf.random_split(0.2)
print(f"Train size {len(train)}\nTest size: {len(test)}" )
features_list = ['degree_v', 'degree_u', 'total_friends', 'common_friends', 'jaccard_coefficient', 'adamic_adar_index', 'friends_measure' ]
cls = tc.classifier.create(train,features=features_list, target="class")
Train size 15911
Test size: 4084
PROGRESS: Creating a validation set from 5 percent of training data. This may take a while.
          You can set ``validation_set=None`` to disable validation tracking.

PROGRESS: The following methods are available for this type of problem.
PROGRESS: BoostedTreesClassifier, RandomForestClassifier, DecisionTreeClassifier, SVMClassifier, LogisticClassifier
PROGRESS: The returned model will be chosen according to validation accuracy.
Boosted trees classifier:
--------------------------------------------------------
Number of examples          : 15115
Number of classes           : 2
Number of feature columns   : 7
Number of unpacked features : 7
+-----------+--------------+-------------------+---------------------+-------------------+---------------------+
| Iteration | Elapsed Time | Training Accuracy | Validation Accuracy | Training Log Loss | Validation Log Loss |
+-----------+--------------+-------------------+---------------------+-------------------+---------------------+
| 1         | 0.019639     | 0.914787          | 0.919598            | 0.501979          | 0.502327            |
| 2         | 0.030255     | 0.921535          | 0.924623            | 0.393752          | 0.396487            |
| 3         | 0.040271     | 0.923917          | 0.920854            | 0.325248          | 0.328521            |
| 4         | 0.051535     | 0.924578          | 0.923367            | 0.279848          | 0.286472            |
| 5         | 0.062766     | 0.928614          | 0.930905            | 0.247168          | 0.257851            |
| 10        | 0.112601     | 0.937413          | 0.922111            | 0.173620          | 0.198396            |
+-----------+--------------+-------------------+---------------------+-------------------+---------------------+
Random forest classifier:
--------------------------------------------------------
Number of examples          : 15115
Number of classes           : 2
Number of feature columns   : 7
Number of unpacked features : 7
+-----------+--------------+-------------------+---------------------+-------------------+---------------------+
| Iteration | Elapsed Time | Training Accuracy | Validation Accuracy | Training Log Loss | Validation Log Loss |
+-----------+--------------+-------------------+---------------------+-------------------+---------------------+
| 1         | 0.021764     | 0.877737          | 0.870603            | 0.342846          | 0.345350            |
| 2         | 0.030993     | 0.916441          | 0.914573            | 0.290101          | 0.295666            |
| 3         | 0.040812     | 0.923652          | 0.912060            | 0.278714          | 0.287122            |
| 4         | 0.049855     | 0.921998          | 0.912060            | 0.276937          | 0.286162            |
| 5         | 0.060722     | 0.922593          | 0.910804            | 0.274622          | 0.282034            |
| 10        | 0.118699     | 0.925041          | 0.919598            | 0.269616          | 0.275334            |
+-----------+--------------+-------------------+---------------------+-------------------+---------------------+
Decision tree classifier:
--------------------------------------------------------
Number of examples          : 15115
Number of classes           : 2
Number of feature columns   : 7
Number of unpacked features : 7
+-----------+--------------+-------------------+---------------------+-------------------+---------------------+
| Iteration | Elapsed Time | Training Accuracy | Validation Accuracy | Training Log Loss | Validation Log Loss |
+-----------+--------------+-------------------+---------------------+-------------------+---------------------+
| 1         | 0.019565     | 0.914787          | 0.919598            | 0.273553          | 0.273217            |
+-----------+--------------+-------------------+---------------------+-------------------+---------------------+
SVM:
--------------------------------------------------------
Number of examples          : 15115
Number of classes           : 2
Number of feature columns   : 7
Number of unpacked features : 7
Number of coefficients    : 8
Starting L-BFGS
--------------------------------------------------------
+-----------+----------+-----------+--------------+-------------------+---------------------+
| Iteration | Passes   | Step size | Elapsed Time | Training Accuracy | Validation Accuracy |
+-----------+----------+-----------+--------------+-------------------+---------------------+
| 0         | 2        | 1.000000  | 0.055003     | 0.521667          | 0.517588            |
| 1         | 5        | 5.000000  | 0.144228     | 0.873172          | 0.883166            |
| 2         | 6        | 5.000000  | 0.190399     | 0.791862          | 0.777638            |
| 3         | 11       | 0.889270  | 0.325526     | 0.854846          | 0.855528            |
| 4         | 13       | 0.889270  | 0.397568     | 0.874429          | 0.880653            |
| 9         | 19       | 1.000000  | 0.644084     | 0.883030          | 0.883166            |
+-----------+----------+-----------+--------------+-------------------+---------------------+
Logistic regression:
--------------------------------------------------------
Number of examples          : 15115
Number of classes           : 2
Number of feature columns   : 7
Number of unpacked features : 7
Number of coefficients      : 8
Starting Newton Method
--------------------------------------------------------
+-----------+----------+--------------+-------------------+---------------------+
| Iteration | Passes   | Elapsed Time | Training Accuracy | Validation Accuracy |
+-----------+----------+--------------+-------------------+---------------------+
| 1         | 2        | 0.052544     | 0.832220          | 0.832915            |
| 2         | 3        | 0.101365     | 0.857029          | 0.863065            |
| 3         | 4        | 0.150279     | 0.875753          | 0.878141            |
| 4         | 5        | 0.199140     | 0.882898          | 0.883166            |
| 5         | 6        | 0.247299     | 0.883890          | 0.884422            |
| 7         | 8        | 0.346505     | 0.883758          | 0.884422            |
+-----------+----------+--------------+-------------------+---------------------+
SUCCESS: Optimal solution found.

PROGRESS: Model selection based on validation accuracy:
PROGRESS: ---------------------------------------------
PROGRESS: BoostedTreesClassifier          : 0.9221105527638191
PROGRESS: RandomForestClassifier          : 0.9195979899497487
PROGRESS: DecisionTreeClassifier          : 0.9195979899497487
PROGRESS: SVMClassifier                   : 0.8831658291457286
PROGRESS: LogisticClassifier              : 0.8844221105527639
PROGRESS: ---------------------------------------------
PROGRESS: Selecting BoostedTreesClassifier based on validation set performance.
In [23]:
features_list = ['degree_v', 'degree_u', 'total_friends', 'common_friends', 'jaccard_coefficient', 'adamic_adar_index', 'friends_measure' ]
cls = tc.classifier.create(train,features=features_list, target="class")
PROGRESS: Creating a validation set from 5 percent of training data. This may take a while.
          You can set ``validation_set=None`` to disable validation tracking.

PROGRESS: The following methods are available for this type of problem.
PROGRESS: BoostedTreesClassifier, RandomForestClassifier, DecisionTreeClassifier, SVMClassifier, LogisticClassifier
PROGRESS: The returned model will be chosen according to validation accuracy.
Boosted trees classifier:
--------------------------------------------------------
Number of examples          : 15115
Number of classes           : 2
Number of feature columns   : 7
Number of unpacked features : 7
+-----------+--------------+-------------------+---------------------+-------------------+---------------------+
| Iteration | Elapsed Time | Training Accuracy | Validation Accuracy | Training Log Loss | Validation Log Loss |
+-----------+--------------+-------------------+---------------------+-------------------+---------------------+
| 1         | 0.019848     | 0.920146          | 0.917085            | 0.500454          | 0.504346            |
| 2         | 0.031966     | 0.922527          | 0.914573            | 0.393697          | 0.402629            |
| 3         | 0.043331     | 0.925240          | 0.919598            | 0.324735          | 0.337694            |
| 4         | 0.056352     | 0.928680          | 0.914573            | 0.277956          | 0.293943            |
| 5         | 0.067251     | 0.929871          | 0.914573            | 0.245897          | 0.267384            |
| 10        | 0.124786     | 0.936619          | 0.917085            | 0.173354          | 0.205300            |
+-----------+--------------+-------------------+---------------------+-------------------+---------------------+
Random forest classifier:
--------------------------------------------------------
Number of examples          : 15115
Number of classes           : 2
Number of feature columns   : 7
Number of unpacked features : 7
+-----------+--------------+-------------------+---------------------+-------------------+---------------------+
| Iteration | Elapsed Time | Training Accuracy | Validation Accuracy | Training Log Loss | Validation Log Loss |
+-----------+--------------+-------------------+---------------------+-------------------+---------------------+
| 1         | 0.017324     | 0.910354          | 0.896985            | 0.286857          | 0.294182            |
| 2         | 0.026087     | 0.914853          | 0.904523            | 0.280528          | 0.295482            |
| 3         | 0.035900     | 0.917433          | 0.893216            | 0.278688          | 0.293904            |
| 4         | 0.044677     | 0.919484          | 0.902010            | 0.274601          | 0.290174            |
| 5         | 0.054180     | 0.920609          | 0.903266            | 0.272888          | 0.288768            |
| 10        | 0.102172     | 0.920741          | 0.905779            | 0.271054          | 0.285946            |
+-----------+--------------+-------------------+---------------------+-------------------+---------------------+
Decision tree classifier:
--------------------------------------------------------
Number of examples          : 15115
Number of classes           : 2
Number of feature columns   : 7
Number of unpacked features : 7
+-----------+--------------+-------------------+---------------------+-------------------+---------------------+
| Iteration | Elapsed Time | Training Accuracy | Validation Accuracy | Training Log Loss | Validation Log Loss |
+-----------+--------------+-------------------+---------------------+-------------------+---------------------+
| 1         | 0.015915     | 0.920146          | 0.917085            | 0.270431          | 0.284441            |
+-----------+--------------+-------------------+---------------------+-------------------+---------------------+
SVM:
--------------------------------------------------------
Number of examples          : 15115
Number of classes           : 2
Number of feature columns   : 7
Number of unpacked features : 7
Number of coefficients    : 8
Starting L-BFGS
--------------------------------------------------------
+-----------+----------+-----------+--------------+-------------------+---------------------+
| Iteration | Passes   | Step size | Elapsed Time | Training Accuracy | Validation Accuracy |
+-----------+----------+-----------+--------------+-------------------+---------------------+
| 0         | 2        | 1.000000  | 0.046014     | 0.515647          | 0.471106            |
| 1         | 5        | 5.000000  | 0.136277     | 0.875620          | 0.884422            |
| 2         | 6        | 5.000000  | 0.181647     | 0.784320          | 0.812814            |
| 3         | 12       | 4.240610  | 0.368782     | 0.884089          | 0.883166            |
| 4         | 17       | 2.991946  | 0.510386     | 0.871254          | 0.879397            |
| 9         | 24       | 1.692704  | 0.782789     | 0.800265          | 0.825377            |
+-----------+----------+-----------+--------------+-------------------+---------------------+
Logistic regression:
--------------------------------------------------------
Number of examples          : 15115
Number of classes           : 2
Number of feature columns   : 7
Number of unpacked features : 7
Number of coefficients      : 8
Starting Newton Method
--------------------------------------------------------
+-----------+----------+--------------+-------------------+---------------------+
| Iteration | Passes   | Elapsed Time | Training Accuracy | Validation Accuracy |
+-----------+----------+--------------+-------------------+---------------------+
| 1         | 2        | 0.051495     | 0.831690          | 0.853015            |
| 2         | 3        | 0.100329     | 0.856765          | 0.875628            |
| 3         | 4        | 0.151532     | 0.875223          | 0.885678            |
| 4         | 5        | 0.200004     | 0.881641          | 0.891960            |
| 5         | 6        | 0.249238     | 0.881972          | 0.893216            |
| 7         | 8        | 0.346695     | 0.881972          | 0.893216            |
+-----------+----------+--------------+-------------------+---------------------+
SUCCESS: Optimal solution found.

PROGRESS: Model selection based on validation accuracy:
PROGRESS: ---------------------------------------------
PROGRESS: BoostedTreesClassifier          : 0.9170854271356784
PROGRESS: RandomForestClassifier          : 0.9057788944723618
PROGRESS: DecisionTreeClassifier          : 0.9170854271356784
PROGRESS: SVMClassifier                   : 0.8253768844221105
PROGRESS: LogisticClassifier              : 0.8932160804020101
PROGRESS: ---------------------------------------------
PROGRESS: Selecting BoostedTreesClassifier based on validation set performance.
In [24]:
r_sf['probability'] = cls.classify(r_sf[features_list])['probability']
r_sf = r_sf.sort([ 'probability'] , ascending=False) 
r_sf['name', 'common_friends', 'probability'].print_rows(100)
r_sf['name', 'common_friends', 'probability'][5000:5100].print_rows(100)
+----------------------+----------------+--------------------+
|         name         | common_friends |    probability     |
+----------------------+----------------+--------------------+
|          T           |       4        | 0.9756927657872438 |
|   MISS SACCHARINE    |       4        | 0.9756927657872438 |
|    CARLTON, JAKE     |       4        | 0.9756927657872438 |
| QUINN, MIRANDA/M'RYN |       4        | 0.9756927657872438 |
|    MARTINEZ, MR.     |       4        | 0.9756927657872438 |
|   MCCORMICK, BARRY   |       4        | 0.9756927657872438 |
|     AUSTIN, GEN.     |       4        | 0.9756927657872438 |
|   WILLIFORD, BRENT   |       4        | 0.9756927657872438 |
|        COACH         |       4        | 0.9756927657872438 |
|    GILMORE, BERT     |       4        | 0.9756927657872438 |
|       CROSSBOW       |       4        | 0.9756927657872438 |
|         DOOP         |       4        | 0.9756927657872438 |
|    GALANNAN, BILL    |       4        | 0.9756927657872438 |
|      CYCLOPS II      |       4        | 0.9756927657872438 |
|    CARLTON, LUCY     |       4        | 0.9756927657872438 |
|        TAKAR         |       4        | 0.9756927657872438 |
|     KANE, SUGAR      |       4        | 0.9756927657872438 |
|     WARP SAVANT      |       4        | 0.9756927657872438 |
| CARRION III/DR. WILL |       4        | 0.9756927657872438 |
|    KINCAID, MOSES    |       4        | 0.9756927657872438 |
|   LONGBOW, DESIREE   |       4        | 0.9756927657872438 |
| STAPHOS, DR. MONICA  |       4        | 0.9756927657872438 |
|       REDJEK/        |       4        | 0.9756927657872438 |
|    ROSETTA STONE     |       4        | 0.9756927657872438 |
|      MINDSTORM/      |       4        | 0.9756927657872438 |
|    FUSION/MARKLEY    |       4        | 0.9756927657872438 |
|    SHERMAN, GEN.     |       4        | 0.9756927657872438 |
|  DANTES, DR. EDWINA  |       4        | 0.9756927657872438 |
|       HARMONY        |       4        | 0.9756927657872438 |
|     MISTER CLEAN     |       4        | 0.9756927657872438 |
|         BLOK         |       4        | 0.9756927657872438 |
| TYPEFACE/GORDON THOM |       4        | 0.9756927657872438 |
|        BARKER        |       4        | 0.9756927657872438 |
|     EARLE, CARYN     |       4        | 0.9756927657872438 |
|     TRI-SENTINEL     |       4        | 0.9756927657872438 |
|      MAJOR LOVE      |       4        | 0.9756927657872438 |
|      QUINN, MR.      |       4        | 0.9756927657872438 |
|    PACE, MICHAEL     |       4        | 0.9756927657872438 |
|   MARTINEZ, AKASHA   |       4        | 0.9756927657872438 |
| PETRIE, JUDGE RICHAR |       4        | 0.9756927657872438 |
|        CASPER        |       4        | 0.9756927657872438 |
| FEIFFER, DEPUTY BEAU |       4        | 0.9756927657872438 |
|       BOLERO/        |       4        | 0.9756927657872438 |
|       LOCKDOWN       |       4        | 0.9756927657872438 |
| SYNARIO/ANGELA BRADF |       4        | 0.9756927657872438 |
|        WENDY         |       4        | 0.9756927657872438 |
|        NUNTOO        |       4        | 0.9756927657872438 |
|         YSSA         |       4        | 0.9756927657872438 |
|     BRAINSTORM/      |       4        | 0.9756927657872438 |
|          A           |       4        | 0.9756927657872438 |
|    BURNING PUDDLE    |       4        | 0.9756927657872438 |
| MCCULLOUGH, DR. ANDR |       4        | 0.9756927657872438 |
|    HORTON, DOUGIE    |       4        | 0.9756927657872438 |
|        TAREE         |       4        | 0.9756927657872438 |
|      CANDLEMAS/      |       4        | 0.9756927657872438 |
|    HOWLER II/ZED     |       4        | 0.9756927657872438 |
| DE LA SALLE, BEATRIC |       4        | 0.9756927657872438 |
|      ROSE III/       |       4        | 0.9756927657872438 |
|    OBLITERATOR II    |       4        | 0.9756927657872438 |
|   WILLIAMS, NILES    |       4        | 0.9756927657872438 |
|    U-GO GIRL/EDIE    |       4        | 0.9756927657872438 |
|        PAKAI         |       4        | 0.9756927657872438 |
|        MR. X         |       4        | 0.9756927657872438 |
|        SPOOKY        |       4        | 0.9756927657872438 |
|      CHIMERA II      |       4        | 0.9756927657872438 |
|         MACE         |       4        | 0.9756927657872438 |
|     STEIN, EMIL      |       4        | 0.9756927657872438 |
| ANARCHIST/TIKE ALICA |       4        | 0.9756927657872438 |
|     MCNAIR, TAD      |       4        | 0.9756927657872438 |
| THERMO/DR. WALTER MI |       4        | 0.9756927657872438 |
| CONTRARES, ANASTASIA |       4        | 0.9756927657872438 |
|        EXILE         |       4        | 0.9756927657872438 |
|    MUNOZ, CARLOS     |       4        | 0.9756927657872438 |
|       KO'ICHI        |       5        | 0.9756927657872438 |
|     YOUNG, AGNES     |       3        | 0.9756927657872438 |
|       PHEMOUS        |       3        | 0.9756927657872438 |
| ORPHAN II/GUY SMITH  |       3        | 0.9756927657872438 |
|   MIRAGE III/MARGE   |       3        | 0.9756927657872438 |
|       CYPRESS/       |       3        | 0.9756927657872438 |
|   PATTERSON, JACK    |       3        | 0.9756927657872438 |
|   ANDERSON, BAXTER   |       3        | 0.9756927657872438 |
|      SOAP, DET.      |       3        | 0.9756927657872438 |
|    SHANE, BRIDGIT    |       3        | 0.9756927657872438 |
|   BRUKNER, ILANEY    |       3        | 0.9756927657872438 |
| ROGERS, CAPT. STEVEN |       3        | 0.9756927657872438 |
|       WALRUSS        |       3        | 0.9756927657872438 |
|       ARCTURUS       |       3        | 0.9756927657872438 |
|  CRIME-WAVE/HOLLIS   |       3        | 0.9756927657872438 |
|    BIRD, TUESDAY     |       3        | 0.9756927657872438 |
|        GRESIG        |       3        | 0.9756927657872438 |
|   LANSING, CHERYL    |       3        | 0.9756927657872438 |
|    POPPA WHEELER     |       3        | 0.9756927657872438 |
|    POWERHOUSE II/    |       3        | 0.9756927657872438 |
|        CHLOE         |       3        | 0.9756927657872438 |
|         KHAT         |       3        | 0.9756927657872438 |
|     COFFIN, RAY      |       3        | 0.9756927657872438 |
| TYNE, ELIZABETH/JANI |       3        | 0.9756927657872438 |
|    REVELATION II/    |       3        | 0.9756927657872438 |
|       GLITTER/       |       3        | 0.9756927657872438 |
|     CHEN, JOANNA     |       3        | 0.9756927657872438 |
+----------------------+----------------+--------------------+
[5174 rows x 3 columns]

+----------------------+----------------+--------------------+
|         name         | common_friends |    probability     |
+----------------------+----------------+--------------------+
|       RAPUNZEL       |       28       | 0.7918307036161423 |
| STORM DOPPELGANGER I |       62       | 0.7880524396896362 |
| SCHEMER/RICHARD FISK |       50       | 0.7857107520103455 |
| DIAMOND LIL/LILLIAN  |       46       | 0.7857107520103455 |
|  HILDY [ASGARDIAN]   |       51       | 0.7857107520103455 |
|   SAPRISTI, JERRY    |       50       | 0.7857107520103455 |
|   WOLVERINE SKRULL   |       49       | 0.7857107520103455 |
|        SPHINX        |       45       | 0.7857107520103455 |
|     MOY, ALYSSA      |       49       | 0.7857107520103455 |
|      BRIGHTWIND      |       49       | 0.7857107520103455 |
|    MASON, LOUISE     |       45       | 0.7857107520103455 |
| FORRESTER, CAPT. ALE |       48       | 0.7857107520103455 |
|     WOLFE, SARA      |       45       | 0.7857107520103455 |
| MIMIC/CALVIN RANKIN  |       48       | 0.7857107520103455 |
| PUMA/THOMAS FIREHEAR |       48       | 0.7857107520103455 |
|    MUGGINS, MAMIE    |       44       | 0.7857107520103455 |
| GLADIATOR/MELVIN POT |       44       | 0.7857107520103455 |
|   YASHIDA, MARIKO    |       47       | 0.7857107520103455 |
|    WATSON, KRISTY    |       48       | 0.7857107520103455 |
| URSA MAJOR/MIKHAIL U |      137       | 0.7817032933235168 |
| ARCLIGHT/PHILLIPA SO |       55       | 0.7792327255010605 |
|     MERCADO, JOY     |       53       | 0.7792327255010605 |
| BATROC/GEORGES BATRO |      106       | 0.7650781869888306 |
| VISION DOPPELGANGER  |       67       | 0.7642746269702911 |
| MOONDRAGON DOPPELGAN |       65       | 0.7642746269702911 |
|      MALICE III      |       67       | 0.7642746269702911 |
|   AQUARIAN/WUNDARR   |       65       | 0.7642746269702911 |
|      DEATHURGE       |       66       | 0.7642746269702911 |
| UNUS THE UNTOUCHABLE |       67       | 0.7642746269702911 |
|   VIBRAXAS/N'KANO    |       69       | 0.7642746269702911 |
| MAELSTROM/MALCOLM ST |       69       | 0.7642746269702911 |
| QUEEN DIVINE JUSTICE |       54       | 0.7611663490533829 |
| TOMBSTONE/LONNIE THO |       41       | 0.7587511092424393 |
| FERAL/MARIA CALLASAN |       61       | 0.7552605271339417 |
| HOBGOBLIN II/RODERIC |       49       | 0.7467918694019318 |
|    PIERCE, DONALD    |       48       | 0.7467918694019318 |
|   ROBERTSON, RANDY   |       50       | 0.7467918694019318 |
|        STRYFE        |       48       | 0.7467918694019318 |
|      VERTIGO II      |       51       | 0.7467918694019318 |
|       ARCADE/        |       52       | 0.7467918694019318 |
| MERRYWEATHER, IRENE  |       47       | 0.7467918694019318 |
| PENANCE/MONET ST. CR |       46       | 0.7467918694019318 |
|         S'YM         |       62       | 0.743953138589859  |
|    GEE/ALEX POWER    |      114       | 0.7348905205726624 |
| SIRYN/THERESA ROURKE |      116       | 0.7348905205726624 |
|        KAINE         |       48       | 0.7284601032733917 |
|  CARDINAL/CLEMDENON  |       43       | 0.7282335758209229 |
|       SHA SHAN       |       44       | 0.7282335758209229 |
|  VENOM/EDDIE BROCK   |       53       | 0.7279938459396362 |
|  ROBERTSON, MARTHA   |       53       | 0.7279938459396362 |
| NIGHTMARE/EDVARD HAB |       64       | 0.713354617357254  |
| LEGION II/DAVID HALL |       66       | 0.713354617357254  |
|     STACY, JILL      |       60       | 0.713354617357254  |
| REJECT/RAN-SAK [DEVI |       61       | 0.713354617357254  |
| SHADOW KING/AMAHL FA |       63       | 0.713354617357254  |
|    WISDOM, PETER     |       65       | 0.713354617357254  |
|      HAMMERHEAD      |       61       | 0.713354617357254  |
| BOX IV/MADISON JEFFR |       68       | 0.713354617357254  |
|         ULIK         |       66       | 0.713354617357254  |
| BLACKWING/JOSEPH MAN |       65       | 0.713354617357254  |
|   WHITE TIGER II/    |       62       | 0.713354617357254  |
|    LUBENSKI, NATE    |       66       | 0.713354617357254  |
| HOBGOBLIN V/JASON PH |       59       | 0.7111376523971558 |
| BATTLESTAR/LEMAR HOS |      103       | 0.7068590521812439 |
| GREY GARGOYLE/PAUL P |      107       | 0.7068590521812439 |
|    REAGAN, RONALD    |      101       | 0.7068590521812439 |
| LANG, CASSANDRA CASS |      106       | 0.7068590521812439 |
| DOMINO III/BEATRICE/ |       93       | 0.7047246098518372 |
| GRANT, GLORIA GLORY  |       84       | 0.7047246098518372 |
| SABRETOOTH/VICTOR CR |      102       | 0.7047246098518372 |
| BRIDGE, GEORGE WASHI |       99       | 0.7047246098518372 |
| SCARECROW/EBENEZER L |       73       | 0.6980403661727905 |
|  EEL/LEOPOLD STRYKE  |       73       | 0.6980403661727905 |
|     KIRBY, JACK      |       73       | 0.6980403661727905 |
|     FLYING TIGER     |       74       | 0.6980403661727905 |
|        KRONOS        |       72       | 0.6980403661727905 |
| DEVOS THE DEVESTATOR |       74       | 0.6980403661727905 |
|       SIKORSKY       |       74       | 0.6980403661727905 |
| POUNDCAKES/MARIAN PO |       72       | 0.6980403661727905 |
| VINDICATOR II DOPPEL |       70       | 0.6980403661727905 |
| HULK DOPPELGANGER II |       77       | 0.6980403661727905 |
| PAIBOK, CAPT. [SKRUL |       76       | 0.6980403661727905 |
| DEATHLOK III/MICHAEL |      106       | 0.6950165629386902 |
|    DRUMM, JERICHO    |       97       | 0.686440646648407  |
| AMERICAN EAGLE III/J |       95       | 0.6788266897201538 |
| MAGIK/ILLYANA RASPUT |      119       | 0.6746248006820679 |
|   ROM, SPACEKNIGHT   |      131       | 0.6528568863868713 |
| BROTHER VOODOO/DANIE |      116       | 0.6496946811676025 |
|    OSBORN, NORMIE    |       63       | 0.6482110917568207 |
|        SELENE        |       75       | 0.6357966661453247 |
| FIREFIST/RUSTY COLLI |       77       | 0.6357966661453247 |
| SKIDS/SALLY BLEVINS  |       69       | 0.6357966661453247 |
|       CALLISTO       |       74       | 0.6357966661453247 |
| SILVER SAMURAI/KENYU |       80       | 0.6357966661453247 |
| MR. SINISTER/NATHAN  |       64       | 0.6357966661453247 |
| AJAK/TECUMOTZIN [ETE |      115       | 0.6302471160888672 |
|    KRO [DEVIANT]     |       80       | 0.6226160228252411 |
|         BOVA         |       80       | 0.6226160228252411 |
|    POWER, DR. JIM    |       77       | 0.6226160228252411 |
|    CUSHING, KATE     |       78       | 0.6226160228252411 |
+----------------------+----------------+--------------------+
[100 rows x 3 columns]

Example 2: Predicting a Vertex's Attributes

A network structure can be utilized to predict various entity's attributes. For example, we can look at a classroom and construct a social network based on it. This later can be used to predict various vertex’s attributes, such as a student’s department or final exam score. Another example is using a network structure to predict an individual’s lifespan. This can be done by converting the individual’s family tree into a network, and using various features, such as parents’ and siblings’ lifespans to construct a classifier that can predict an individual’s lifespan. In this section, we show a simple example of how to use networks to complete missing data. We will utilize the WikiTree data to construct a network. Then, we use the network to predict an individual’s place of birth.

First let’s create a network with users’ place of birth:

Note: Due to privacy concerns, you need to ask permission in order to download the WikiTree dataset.

In [35]:
import turicreate as tc
import networkx as nx

sf = tc.SFrame.read_csv('./datasets/wikitree/dump_people_users.csv', delimiter="\t", verbose=False)['User ID','Father', 'Mother', 'Birth Location']
sf = sf[sf.apply(lambda r: r['Father'] != 0 and r['Mother'] != 0)]
sf['Birth Location'] = sf['Birth Location'].apply(lambda s: s.split(",")[-1] if len(s) > 0 else "") 
sf.materialize()

We loaded the data to an SFrame object. Next, let's join tables to create an SFrame object with data regarding the sibling connections:

In [36]:
sf2 = sf.join(sf, on=["Father"])
sf2 = sf2.remove_columns(["Mother.1", "Birth Location.1"])
sf2 = sf2[sf2['User ID'] != sf2['User ID.1']]
sf2 = sf2.rename({'User ID.1':'Sibling'})
In [37]:
sf3 = sf.join(sf, on=["Mother"])
sf3 = sf3.remove_columns(["Father.1", "Birth Location.1"])
sf3 = sf3[sf3['User ID'] != sf3['User ID.1']]
sf3 = sf3.rename({'User ID.1':'Sibling'})

Now, we can create a single SFrame with all the links:

In [38]:
links_sf = sf2['User ID', 'Father']
links_sf = links_sf.rename({'User ID': "User1", "Father": "User2"})

t_sf = sf2['User ID', 'Mother']
t_sf = t_sf.rename({'User ID': "User1", "Mother": "User2"})
links_sf = links_sf.append(t_sf)

t_sf = sf2['User ID', 'Sibling']
t_sf = t_sf.rename({'User ID': "User1", "Sibling": "User2"})
links_sf = links_sf.append(t_sf)

t_sf = sf3['User ID', 'Sibling']
t_sf = t_sf.rename({'User ID': "User1", "Sibling": "User2"})
links_sf = links_sf.append(t_sf)

links_sf
Out[38]:
User1 User2
57 55
63 249
94 249
288 66
289 66
291 66
87 425
102 425
115 112
116 112
[91328716 rows x 2 columns]
Note: Only the head of the SFrame is printed.
You can use print_rows(num_rows=m, num_columns=n) to print more rows and columns.

Let's create an SGraph object using the links and vertices data:

In [39]:
links_sf = links_sf.unique()
v_sf = sf['User ID','Birth Location']
sg = tc.SGraph(vertices=v_sf, edges=links_sf, vid_field='User ID', src_field='User1', dst_field='User2')
sg.summary()
Out[39]:
{'num_edges': 31717439, 'num_vertices': 6146450}

Now, we can use the SGraph object to predict a vertex's location based on the majority of its neighborhood:

In [40]:
from collections import Counter
import random
def predict_location_according_to_majority(v_id):
    d = {}
    try:
        g = sg.get_neighborhood(v_id)
        locations = []
    except:
        return None
    for r in g.vertices:
        if r["__id"] == v_id:
            d['actual'] = r['Birth Location']
        else:
            locations.append(r['Birth Location'])
    c = Counter(locations)
    d['predict'] = c.most_common(1)
    
    return d
v_list = list(v_sf["User ID"])
random.shuffle(v_list)
v_list = v_list[:20]
for i in v_list:    
    p = predict_location_according_to_majority(i)
    if p is None:
        continue
    print("Location: %s"  % (p))
Location: {'actual': ' Scotland', 'predict': [(' Scotland', 3)]}
Location: {'actual': ' Canada', 'predict': [(' Canada', 7)]}
Location: {'actual': ' United Kingdom', 'predict': [(None, 2)]}
Location: {'actual': 'Ollie Keokuk Co Iowa', 'predict': [('Ollie Keokuk Co Iowa', 7)]}
Location: {'actual': '', 'predict': [(None, 2)]}
Location: {'actual': ' USA', 'predict': [('', 1)]}
Location: {'actual': ' Sicily', 'predict': [(' Sicily', 4)]}
Location: {'actual': ' United States', 'predict': [('Ohio', 4)]}
Location: {'actual': '', 'predict': [('', 6)]}
Location: {'actual': '', 'predict': [('', 6)]}
Location: {'actual': ' Kentucky', 'predict': [(None, 2)]}
Location: {'actual': ' Netherlands', 'predict': [(None, 1)]}
Location: {'actual': '', 'predict': [('', 8)]}
Location: {'actual': ' United States', 'predict': [(' United States', 7)]}
Location: {'actual': '', 'predict': [('Pennsylvania', 5)]}
Location: {'actual': ' Virginia', 'predict': [('Louisa Co. Va.', 3)]}
Location: {'actual': ' USA', 'predict': [(' USA', 3)]}
Location: {'actual': 'New Zealand', 'predict': [('New Zealand', 5)]}
Location: {'actual': ' United States', 'predict': [(' United States', 6)]}
Location: {'actual': ' U.S.A.', 'predict': [('Virginia', 2)]}

Example 3: Identifying Anomalous/Key Vertices

We can use a network structure to identify anomalous entities. For example, we can identify an anomalous vertex, which could be a spammer, socialbot, celebrity, or key employee, by identifying vertices which are connected to many different communities.

Let's analyze the Enron Emails dataset and construct the email networks by parsing the emails' To and From headers. Using the constructed Enron network, we will try to find methods to identify key employees in the company:

In [41]:
!mkdir ./datasets/enron
!wget -O ./datasets/enron/enron_mail_20150507.tar.gz https://www.cs.cmu.edu/~./enron/enron_mail_20150507.tar.gz
!tar -zxvf ./datasets/enron/enron_mail_20150507.tar.gz -C ./datasets/enron/
!ls ./datasets/enron
mkdir: ./datasets/enron: File exists
--2020-03-15 17:51:34--  https://www.cs.cmu.edu/~./enron/enron_mail_20150507.tar.gz
Resolving www.cs.cmu.edu (www.cs.cmu.edu)... 128.2.42.95
Connecting to www.cs.cmu.edu (www.cs.cmu.edu)|128.2.42.95|:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: 443254787 (423M) [application/x-gzip]
Saving to: ‘./datasets/enron/enron_mail_20150507.tar.gz’

./datasets/enron/en 100%[===================>] 422.72M  36.7KB/s    in 2h 36m  

2020-03-15 20:27:45 (46.2 KB/s) - ‘./datasets/enron/enron_mail_20150507.tar.gz’ saved [443254787/443254787]

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