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A social network caught in the Web

Lada Adamic and Eytan Adar (HP Labs, Palo Alto, CA)Orkut Buyukkokten (Google)

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Outline

Intro to Club Nexus

Profiles

Nexus Net

Similarity and distance

Association by similarity

Nexus Karma

Conclusions

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Profiles:

status (UG or G) yearmajor or departmentresidencegender

Personality (choose 3 exactly):you funny, kind, weird, …friendship honesty/trust, common interests, commitment, …romance - “ -freetime socializing, getting outside, reading, …support unconditional accepters, comic-relief givers, eternal optimists

Interests (choose as many as apply)books mystery & thriller, science fiction, romance, …movies western, biography, horror, …music folk, jazz, techno, …social activities ballroom dancing, barbecuing, bar-hopping, …land sports soccer, tennis, golf, …water sports sailing, kayaking, swimming, …other sports ski diving, weightlifting, billiards, …

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Finding correlations between user attributes

Are people who consider themselves funny also more likely to enjoy comedies?

518 funny users74 % of users overall like comedies416 (80% of) funny users like comedies,

this is 3.4 standard deviations (=10) above expected (383)

Z score = 3.4

Z scores with absolute value > 2 are significant at the p = 0.05 level.3.4 is significant at the 0.0003 level

small differences (10%) can be significant.

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book business landsport tennis other weightlifting social barbecuing watersport boating, jet skiing, water skiing

successful

free time fulfilling commitments, catching up on chores and things

book sex movie erotic & softcore, gay & lesbian,

independent music funk, jungle, reggae, trance other skateboarding

not responsible

social raving

book art & photography, philosophy, fiction & literature, classics

music folk, bluegrass/rural, jazz

creative

movie art, documentary, independent

Personality and tastes (just a few examples)

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Major and personalitypersonality (% of total) major

free time: learning (17%) Physics (46%), Philosophy (37%), Math (31%),

EE (26%), CS (24%)

free time: reading (26%) English (55%)

free time: staying at home (8%) History (24%)

free time: doing anything exciting (52%)

undecided/undeclared (62%)

you: weird (12%) Physics (34%), Math (28%), EE (18%)

you: intelligent (32%) Philosophy (59%), CS (42%)

you: successful (4%) CS (7%)

you: socially adaptable (14%) STS (46%)

you: attractive (16%) Political Science (29%), International Relations (25%)

you: lovable (12%) Political Science (24%)

you: kind (25%) Public Policy (45%)

you: funny (25%) Philosophy (6%)

you: fun (26%) Human Biology (38%)

you: creative (22%) Product Design (62%), English (42%)

you: sexy (8%) English (18%), EE (2%)

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preference Male users Female users book computers, science fiction, professional &

technical, science, business, politics, philosophy, sports, adventure

romance, fiction & literature, health mind & body, cooking, art & photography, entertainment, mystery & thriller, psychology, classics

landsport football, frisbee golfing, table tennis, golf, baseball, basketball, cricket, fencing, racquetball, squash, tennis, soccer, wrestling

gymnastics, field hockey, softball

movie science fiction, war, action, spy film, erotic & softcore, adventure, anime, sports, western

romance, family, drama, musical, performing arts, comedy, independent

music heavy metal soul/R&B, pop, country/western, rap/hip hop, folk, latin

other computer gaming, weightlifting, billiards, ultimate frisbee, mountain biking, paintballing, laser gaming, bicycling

aerobics, ice skating, jogging

social barbecuing, raving, hot tubbing hip-hop dancing, lating dancing, clubbing

watersport fishing, sailing swimming personality freetime learning, doing physical challenging activities catching up on chores and things,

socializing friendship mutual friends, common interests,

appearance/look, sex laughter, honesty/trust, communication

romance appearance/look, sex, physical attraction laughter, honesty/trust

support the eternal optimists, the give-it-to-you-straight people, i've-been-down-and-dirty-a-few-times-myself people

unconditional accepters, the listeners, chicken-soup people

you intelligent fun, lovable, friendly

Gender Differences

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0 20 40 60 80 1000

50

100

150

200

250

number of links

nu

mb

er

of u

sers

with

so

ma

ny

links

100

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100

101

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number of links

num

ber

of u

sers

Degree Distribution for Nexus Net 2469 users, average degree 8.2

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1 2 3 4 5 6 7 8 9 10 11 12 130

2

4

6

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10

12x 10

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distance

pa

irs

of u

sers

average distance = 4.0

Shortest paths between users

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Clustering and betweenness

Clustering or transitivity: how many of the user’s friends are friends themselves

C = # links between friends

(# friends)* (# friends - 1)/2

c = 0.17 for Club Nexus

Other findings:

people who list more buddies list more preferences/activities

edges with high betweenness lie between dissimilar people ( = -0.2)

people with high betweenness have more links ( = 0.7)

- “ - have lower clustering coefficients ( = -0.12)

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Similarity and distance

year is more important for undergradsdepartment is more important for grads

1 2 3 4 5 6 7 80

0.2

0.4

0.6

0.8

1

distance between users in hops

frac

tio

n o

f si

mila

r u

sers

G residenceUG residenceG departmentUG majorG yearUG yearG statusUG status

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users who like Aall users

Association ratios

p = (# users who like A)/(total #users)L = # connections A users havem = expected number of links to other A users = L*pr = (# links between A users)/m

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personality association ratio

Z score # users # connections

sexy 1.46 5.47 204 192 talented 1.40 5.17 213 210 fun 1.25 11.22 633 1852 weird 1.25 4.32 286 332 lovable 1.22 4.20 292 406 unique 1.11 4.15 547 1194 funny 1.10 4.06 619 1474 friendly 1.10 7.55 1024 4024 socially adaptable 1.09 2.12 342 482

attractive 1.07 1.76 406 522 creative 1.04 1.48 541 982 intelligent 1.01 0.42 779 1848 responsible 0.99 -0.28 500 686 kind 0.99 -0.44 625 1226 competent 0.92 -1.40 294 226 successful 0.70 -1.57 99 18

Personality and association ratio

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high association low association

book gay & lesbian, professional & technical, computers, teen, sex, sports

history, fiction & literature, outdoor & nature

movie

genres

gay & lesbian, performing arts, religion, erotic & softcore, sports

drama, mystery, documentary, comedy

music genres

gospel, jungle, bluegrass/rural, heavy metal, trance

pop, classical, rock

land sport lacrosse, field hockey, wrestling, cricket tennis, martial arts, bicycling, racquetball

water sport

synchronized swimming, diving, crew swimming, fishing windsurfing

social raving, ballroom dancing, Latin dancing partying, camping

Interests and association ratios

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Nexus Karma

Rank how ‘trusty’, ‘nice’, ‘cool’, and ‘sexy’ your buddiesare on a scale of 1 to 4

446 users ranked 1735 different friends

correlations between scores given (users were ranked as‘3,3,3,3’ more often than ‘1,4,2,3’

average scores: nice (3.37), trusty (3.22), cool (3.13), sexy(2.83)

trusty--nice and cool--sexy more highly correlated ( = 0.7) vs.trusty--sexy and nice--sexy ( = 0.4)

no relationship between average score received and # of friendsnegative correlation between average score given and # of friends

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How users view themselves vs. how others view them

trusty (3.22)nice(3.37)

cool(3.13)

sexy(2.83)

responsible

3.36 3.02 2.67

sexy 3.10 3.23 3.03

attractive 3.09 3.25 2.93

kind 3.34 3.46

friendly 3.44

weird 2.67

funny 3.31

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Additional insights from Nexus Karma

Users receiving higher ‘nice’ scores give higher ‘trusty’, ‘nice’, and ‘cool’scores ( = 0.14-0.17)

If one user gives another user a higher ‘trusty’ or ‘nice’ score than their other friends, that same friend is more likely to reciprocate.

Users who share friends are more likely to give each other high scores( = 0.10-0.13)

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Conclusions

Learn about real world social networks from online community

Less effort than traditional social network survey methods,almost a side-effect of digital nature of interactions

Although most results not surprising, data is very rich - opportunity to simulate search and information spread

Karma data can be used to study online reputation mechanisms

Longitudinal data can be used to study network evolution

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To find out more:

Information dynamics group (IDL) at HP Labs:http://www.hpl.hp.com/shl/

Paper at:http://www.hpl.hp.com/shl/social/

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free time activity association ratio

Z score # users # connections

fulfilling commitments

1.34 9.30 398 826

socializing 1.12 21.12 1660 11374 catching up on chores and things

1.09 2.71 494 850

learning 1.07 1.82 420 536 doing anything exciting

1.07 8.05 1280 6278

watching TV 1.07 1.85 415 602 reading 1.02 0.66 631 1186 getting outside 1.01 0.97 940 2882 staying at home 0.97 -0.32 209 126 alone 0.96 -0.93 380 398 doing physical challenging activities

0.96 -1.46 577 878

Free time activity and association ratios