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Predicting positive and negative links in online social networks

Authors:

Daniel Huttenlocher,

Jon KleinbergAuthors Info & Claims

WWW '10: Proceedings of the 19th international conference on World wide web

Pages 641 - 650

https://doi.org/10.1145/1772690.1772756

Published: 26 April 2010 Publication History

Abstract

We study online social networks in which relationships can be either positive (indicating relations such as friendship) or negative (indicating relations such as opposition or antagonism). Such a mix of positive and negative links arise in a variety of online settings; we study datasets from Epinions, Slashdot and Wikipedia. We find that the signs of links in the underlying social networks can be predicted with high accuracy, using models that generalize across this diverse range of sites. These models provide insight into some of the fundamental principles that drive the formation of signed links in networks, shedding light on theories of balance and status from social psychology; they also suggest social computing applications by which the attitude of one user toward another can be estimated from evidence provided by their relationships with other members of the surrounding social network.

References

[1]

T. Antal, P. Krapivsky, and S. Redner. Social balance on networks. Physica D, 224(130), 2006.

[2]

M. J. Brzozowski, T. Hogg, and G. Szabó. Friends and foes: ideological social networking. In Proc. 26th CHI, 2008.

Digital Library

[3]

M. Burke and R. Kraut. Mopping up: Modeling wikipedia promotion decisions. In Proc. CSCW, 2008.

Digital Library

[4]

D. Cartwright and F. Harary. Structure balance: A generalization of Heider's theory. Psych. Rev., 63, 1956.

[5]

M. Chudnovsky, P. Seymour, and B. D. Sullivan. Cycles in dense digraphs. Combinatorica, 28(1):1--18, 2008.

Digital Library

[6]

D. Cosley, D. Frankowski, S. B. Kiesler, L. G. Terveen, and J. Riedl. How oversight improves member-maintained communities. In Proc. 23rd CHI, pages 11--20, 2005.

Digital Library

[7]

J. A. Davis. Structural balance, mechanical solidarity, and interpersonal relations. Am. J. Soc., 68:444--462, 1963.

[8]

R. V. Guha, R. Kumar, P. Raghavan, and A. Tomkins. Propagation of trust and distrust. In Proc. 13th WWW, 2004.

Digital Library

[9]

V. Guruswami, R. Manokaran, and P. Raghavendra. Beating the random ordering is hard: Inapproximability of maximum acyclic subgraph. In Proc. 49th IEEE FOCS, 2008.

Digital Library

[10]

Z. Gyöngyi, H. Garcia-Molina, and J. Pedersen. Combating web spam with trustrank. In VLDB '04, 2004.

Digital Library

[11]

F. Heider. Attitudes and cognitive organization. J. Psych., 21:107--112, 1946.

[12]

S. D. Kamvar, M. T. Schlosser, and H. G. Molina. The eigentrust algorithm for reputation management in p2p networks. In Proc. 12th WWW, pages 640--651. ACM, 2003.

Digital Library

[13]

J. Kunegis, A. Lommatzsch, and C. Bauckhage. The Slashdot Zoo: Mining a social network with negative edges. In Proc. 18th WWW, pages 741--750, 2009.

Digital Library

[14]

C. Lampe, E. Johnston, and P. Resnick. Follow the reader: filtering comments on slashdot. In Proc. 25th CHI, 2007.

Digital Library

[15]

J. Leskovec, D. Huttenlocher, and J. Kleinberg. Signed networks in social media. In Proc. 28th CHI, 2010.

Digital Library

[16]

D. Liben-Nowell and J. Kleinberg. The link-prediction problem for social networks. J. Amer. Soc. Inf. Sci. and Tech., 58(7):1019--1031, 2007.

Digital Library

[17]

S. Marvel, S. Strogatz, and J. Kleinberg. Energy landscape of social balance. Physical Review Letters, 103, 2009.

[18]

P. Massa and P. Avesani. Controversial users demand local trust metrics: an experimental study on epinions.com community. In AAAI '05, pages 121--126. AAAI Press, 2005.

Digital Library

[19]

M. E. J. Newman. The structure and function of complex networks. SIAM Review, 45:167--256, 2003.

Digital Library

[20]

B. Pang and L. Lee. Opinion Mining and Sentinment Analysis. Number 2(1--2) in Foundations and Trends in Information Retrieval. Now Publishers, 2008.

Digital Library

[21]

P. Resnick and H. R. Varian. Recommender systems. Comm. ACM, 40(3):56--58, 1997.

Digital Library

[22]

M. Richardson, R. Agrawal, and P. Domingos. Trust management for the semantic web. In Intl. Semantic Web Conference, 2003.

Digital Library

[23]

S. Wasserman and K. Faust. Social Network Analysis: Methods and Applications. Cambridge Univ. Pr., 1994.

[24]

F. Wu and B. A. Huberman. How public opinion forms. In Proc. 4th WINE, pages 334--341, 2008.

Digital Library

[25]

L. Xiong and L. Liu. Peertrust: supporting reputation-based trust for peer-to-peer electronic communities. IEEE Trans. Knowl. Data Engr., 16(7):843--857, 2004.

Digital Library

Cited By

Liu Y(2025)Signed Latent Factors for Spamming Activity DetectionIEEE Transactions on Information Forensics and Security10.1109/TIFS.2024.351657320(651-664)Online publication date: 2025
https://doi.org/10.1109/TIFS.2024.3516573
Abdeddine AMekouar LIraqi Y(2025)A Generic Framework for Mobile Crowdsensing: A Comprehensive SurveyIEEE Access10.1109/ACCESS.2025.352673913(9134-9170)Online publication date: 2025
https://doi.org/10.1109/ACCESS.2025.3526739
Lee HLu PSha HHuang H(2025)Triadic balance and network evolution in predictive models of signed networksScientific Reports10.1038/s41598-024-85078-515:1Online publication date: 20-Jan-2025
https://doi.org/10.1038/s41598-024-85078-5
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Index Terms

Predicting positive and negative links in online social networks
1. Information systems
  1. Information systems applications
    1. Data mining

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Information & Contributors

Information

Published In

cover image ACM Other conferences

WWW '10: Proceedings of the 19th international conference on World wide web

April 2010

1407 pages

ISBN:9781605587998

DOI:10.1145/1772690

General Chairs:
Michael Rappa
North Carolina State University, USA
,
Paul Jones
University of North Carolina at Chapel Hill, USA
,
Program Chairs:
Juliana Freire
University of Utah, USA
,
Soumen Chakrabarti
Indian Institute of Technology, India

Copyright © 2010 International World Wide Web Conference Committee (IW3C2).

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 26 April 2010

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WWW '10

WWW '10: The 19th International World Wide Web Conference

April 26 - 30, 2010

North Carolina, Raleigh, USA

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Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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Liu Y(2025)Signed Latent Factors for Spamming Activity DetectionIEEE Transactions on Information Forensics and Security10.1109/TIFS.2024.351657320(651-664)Online publication date: 2025
https://doi.org/10.1109/TIFS.2024.3516573
Abdeddine AMekouar LIraqi Y(2025)A Generic Framework for Mobile Crowdsensing: A Comprehensive SurveyIEEE Access10.1109/ACCESS.2025.352673913(9134-9170)Online publication date: 2025
https://doi.org/10.1109/ACCESS.2025.3526739
Lee HLu PSha HHuang H(2025)Triadic balance and network evolution in predictive models of signed networksScientific Reports10.1038/s41598-024-85078-515:1Online publication date: 20-Jan-2025
https://doi.org/10.1038/s41598-024-85078-5
Singh SSrivastava DVerma MMuhuri S(2025)Information diffusion analysis: process, model, deployment, and applicationThe Knowledge Engineering Review10.1017/S026988892400010939Online publication date: 22-Jan-2025
https://doi.org/10.1017/S0269888924000109
Yan LChen XLiu YWang ZLi X(2025)An efficient structure-driven multiplex network dismantling approach based on network percolationExpert Systems with Applications10.1016/j.eswa.2024.126177267(126177)Online publication date: Apr-2025
https://doi.org/10.1016/j.eswa.2024.126177
Heshmati AMeghdadi MAfsharchi MAhmadian S(2025)SiSRS: Signed social recommender system using deep neural network representation learningExpert Systems with Applications10.1016/j.eswa.2024.125205259(125205)Online publication date: Jan-2025
https://doi.org/10.1016/j.eswa.2024.125205
Betzer LSuppakitpaisarn VHillebrand QKiyavash NMooij J(2024)Publishing number of walks and katz centrality under local differential privacyProceedings of the Fortieth Conference on Uncertainty in Artificial Intelligence10.5555/3702676.3702694(377-393)Online publication date: 15-Jul-2024
https://dl.acm.org/doi/10.5555/3702676.3702694
Pearce-Crump EKnottenbelt WSalakhutdinov RKolter ZHeller KWeller AOliver NScarlett JBerkenkamp F(2024)Graph automorphism group equivariant neural networksProceedings of the 41st International Conference on Machine Learning10.5555/3692070.3693693(40051-40077)Online publication date: 21-Jul-2024
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Şimşek A(2024)Estimating the Expected Influence Capacities of Nodes in Complex Networks under the Susceptible-Infectious-Recovered ModelBitlis Eren Üniversitesi Fen Bilimleri Dergisi10.17798/bitlisfen.1407941Online publication date: 20-Mar-2024
https://doi.org/10.17798/bitlisfen.1407941
Ying JYan CWu S(2024)New Discovery of the Emergence Mechanism of High Clustering CoefficientsComplexity10.1155/cplx/10397522024:1Online publication date: 19-Dec-2024
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