A glimpse on social influence and link prediction in OSNs

A glimpse on social influence and link prediction in OSNs

Keywords : link creation, link prediction, homophily, social influence, aNobii

Workshop on Data Driven Dynamical Networks

Speaker:

Luca Maria Aiello, PhD studentUniversità degli Studi di TorinoComputer Science [email protected]

Acknowledgments

Giancarlo RuffoRossano Schifanella

Alain BarratCiro Cattuto

People:

Università degli Studi di Torino ISI Foundation

School of Informatics and Computing, Indiana University

Filippo Menczer

3

Dynamics leading to link creation

Several theories from sociology◦Self-interest◦Mutual-interest◦Exchange◦Contagion (influence)◦Balance◦Homophily◦Proximity

28/09/2010 Les Houches 2010 - Luca Maria Aiello, Università degli Studi di Torino

Food networksCollaboration networks

Social media

2nd part:exploit the observations on these phenomena to predict future links


• Dataset• Topical overlap

• Homophily and influence

• Link prediction

• Conclusions

Outline




• Link prediction

• Conclusions

Outline

6

Social network for bookwormsData-driven analysis on anobii.com


Social network◦Directed◦Friendship + neighborhood

Profile features◦ Library and wishlist◦Groups ◦Tags

4th snapshot Friendship Neighborhood Union

Nodes 74,908 54,590 86,800

Links 268,655 429,482 697,910

6 snapshots, 15 days apartFull giant connected component

Basic statistics

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Broad distributionsPositive correlations between

connectivity and activityAssortativity

103

102

101

100

100 101 102 103kout

ng(kout)nb(kout)nw(kout)

8

Triadic closure

Reciprocation is strong (exchange)Users tend to choose “friends of their friends”

as new friends (balance)


Direct Reciprocated BidirectionalClosure

Doubleclosure

75% 20% 25%30% 10%

Classification of new links at time t+1 between nodes already present at time t (t ∈ {1,…,5})




• Link prediction

• Conclusions

Outline

10

Profile similarity vs. social distance

Topical overlapStatistical correlation because of assortative biases?Null model to discern real overlap from purely statistical

effects◦No topical overlap other than that caused by statistical

mixing patters28/09/2010 Les Houches 2010 - Luca Maria Aiello, Università degli Studi di Torino

vnun

bbvu

bb

b vub

,

Does similarity between user profiles depend on the social distance?

SocialCom 2010 - Luca Maria Aiello, Università degli Studi di Torino 11

Geographical overlap

Null model test with random link rewire

Country-level overlap due to language barriers

City level overlap

22/08/2010




• Link prediction

• Conclusions

Outline

13

Causality between similarity and link creation

Topical overlap is observed for all profile features

Three possible explanations:1. Homophily (people connect with similar people)2. Social influence (social connection conveys

similarity)3. Mixture of the two

Explore the causality relationship between profile similarity and social linking


What is the cause of topical overlap?

14

Similarity link creation (homophily)


⟨ncb⟩ σb ⟨ncg⟩ σg

duv = 2 9.5 0.02 1.12 0.05

u → v 12.9 0.04 1.10 0.08

u ↔ v 18.5 0.04 1.67 0.11Closure 18.2 0.04 1.81 0.10Dbl closure 23.4 0.05 1.20 0.12

Average similarity of pairs forming new links between t and t+1 (t=4), compared with average similarity of all the pairs at distance 2 at time t

Pairs that are going to get connected show a substantially higher similarity

15

Link creation similarity (influence)


Evolution of the similarity between pairs linking together at different times

Groups

Books

16

Summary

Theories to explain link creation◦Self-interest◦Mutual-interest◦Exchange Reciprocity in linking◦Contagion Social influence◦Balance Triangle closure◦Homophily For all profile features◦Proximity Geographical and on social graph


Can we exploit the observations on these phenomena to predict future links?




• Link prediction

• Conclusions

Outline

18

Link prediction

Snapshots at time t and t+1Predict links created between t and t+1 given the

whole information at time tSupervised learning approach to combine profile and

structural features


Pair Id Library sim. Common neighbors Will be connected?

1 0.56 18 1

2 0.11 5 0

3 0.71 36 1

Learning set example

FeaturesProfile

◦ Library (cosine)◦ Groups (cosine)◦ Groups (size)

◦ Gender {0,1}◦ Town {0,1}◦ Age (|age1 – age2|)◦ Country {0,1}◦ Vocabulary (cosine)◦ Wishlists (cosine)◦ Tagging behavior

28/09/2010 Les Houches 2010 - Luca Maria Aiello, Università degli Studi di Torino 19

Structural◦ Common neighbors◦ Distance on graph◦ Preferential attachment

◦ Resource allocation

◦ Local path

)()( )(1

yxzxy zks

)()( ykxksxy

]1,0[,32 AAS

)()( ||1

yGxGgxy gs

20

Link prediction: preliminary results


Rotation forest, 10-fold cross-validation, balanced sets

Rotation forest, 10-fold cross-validation, unbalanced sets

Precision Recall F-measure AUC

Structural 0.782 0.778 0.777 0.838

Topical 0.746 0.746 0.746 0.82

Complete 0.827 0.826 0.826 0.9

Complete

K-ratio Precision Recall F-measure AUC

1:1 0.827 0.826 0.826 0.9

1:10 0.934 0.94 0.933 0.897

1:100 0.988 0.991 0.987 0.86




• Link prediction

• Conclusions

Outline

22

Conclusions and future work

Theories on social network growth are verifiedCausality between similarity and social

connectionEffective link detection/prediction

◦Topical information seems to be predictive as well as structural information

RFC:◦ Link prediction sampling/evaluation procedure◦New challenges in prediction


Speaker: Luca Maria [email protected]

www.di.unito.it/~aiello

Thank you for your attention!

Workshop on Data Driven Dynamical Networks

Reference:L. M. Aiello, A. Barrat, C. Cattuto, G. Ruffo, R. Schifanella "Link creation and profile alignment in the aNobii social network"In SocialCom'10: Proceedings of the 2nd IEEE International Conference on Social Computing, Minneapolis, MN, USA, August 2010

http://www.iisocialcom.org/conference/socialcom2010

http://www.iisocialcom.org/conference/socialcom2010

A glimpse on social influence and link prediction in OSNs

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