Encyclopedia of Social Network Analysis and Mining

Living Edition
| Editors: Reda Alhajj, Jon Rokne

Behavior Modeling in Social Networks

Living reference work entry
DOI: https://doi.org/10.1007/978-1-4614-7163-9_110203-1

Synonyms

Glossary

CF

Collaborative filtering

LDA

Latent Dirichlet Allocation: an effective topic model

OSNs

Online social networks

Power law

A functional relationship between two quantities where a relative change in one quantity results in a proportional relative change in the other quantity: one quantity varies as a power of another

RSs

Recommender systems

SBD

Suspicious behavior detection: detecting fake reviews, fake social accounts, spammers, fake relationships, and fraudsters

Social context

Contextual factors that determine users’ behaviors in social environments such as influence, trust, and preference

UGC

User-generated content

Definition

Behavior modeling in social networks is a core framework of the “data-to-knowledge-to-service” pipeline with behavioral data as input to support services and systems of the OSNs including precise recommendation and spam and fraud detection (Fig. 1). The main...
This is a preview of subscription content, log in to check access.

References

  1. Balabanović M, Shoham Y (1997) Fab: content-based, collaborative recommendation. Commun ACM 40(3):66–72CrossRefGoogle Scholar
  2. Beutel A, Xu W, Guruswami V, Palow C, Faloutsos C (2013) Copycatch: stopping group attacks by spotting lockstep behavior in social networks. In: Proceedings of the 22nd international conference on World Wide Web, Rio de Janeiro, pp 119–130, 13–17 May 2013Google Scholar
  3. Blei DM, Ng AY, Jordan MI (2003) Latent dirichlet allocation. J Mach Learn Res 3:993–1022MATHGoogle Scholar
  4. Bond R, Smith PB (1996) Culture and conformity: a meta-analysis of studies using asch’s (1952b, 1956) line judgment task. Psychol Bull 119(1):111CrossRefGoogle Scholar
  5. Chirita PA, Diederich J, Nejdl W (2005) MailRank: using ranking for spam detection. In: Proceedings of the 14th ACM international conference on Information and knowledge management, Bremen, 31 Oct–5 Nov 2005, pp 373–380Google Scholar
  6. Cui P, Jin S, Yu L, Wang F, Zhu W, Yang S (2013) Cascading outbreak prediction in networks: a data-driven approach. In: Proceedings of the 19th ACM SIGKDD international conference on knowledge discovery and data mining, Chicago, 11–14 Aug 2013, pp 901–909Google Scholar
  7. Holland PW (1986) Statistics and causal inference. J Am Stat Assoc 81(396):945–960MathSciNetCrossRefMATHGoogle Scholar
  8. Jiang M, Cui P, Wang F, Yang Q, Zhu W, Yang S (2012a) Social recommendation across multiple relational domains. In: Proceedings of ACM international Conference on information and knowledge management (CIKM), Maui, 29 Oct–2 Nov 2012, pp 1422–1431Google Scholar
  9. Jiang M, Cui P, Liu R, Yang Q, Wang F, Zhu W, Yang S (2012b) Social contextual recommendation. In: Proceedings of ACM international conference on information and knowledge management (CIKM), Maui, 29 Oct–2 Nov 2012, pp 45–54Google Scholar
  10. Jiang M, Cui P, Wang F, Xu X, Zhu W, Yang S (2014a) FEMA: flexible evolutionary multi-faceted analysis for dynamic behavioral pattern discovery. In: Proceedings of ACM SIGKDD international conference on knowledge discovery and data mining (SIGKDD), New York, 24–27 Aug 2014, pp 1186–1195Google Scholar
  11. Jiang M, Cui P, Wang F, Zhu W, Yang S (2014b) Scalable recommendation with social contextual information. IEEE Trans Knowl Data Eng (TKDE) 26(11):2789–2802CrossRefGoogle Scholar
  12. Jiang M, Cui P, Beutel A, Faloutsos C, Yang S (2014c) CatchSync: catching synchronized behavior in large directed graphs. In: The 20th ACM SIGKDD international conference on knowledge discovery and data mining (KDD), New York, 24–27 Aug 2014, pp 941–950Google Scholar
  13. Jiang M, Cui P, Beutel A, Faloutsos C, Yang S (2014d) Inferring strange behavior from connectivity pattern in social networks. In: Proceedings of the 18th Pacific-Asia conference on knowledge discovery and data mining (PAKDD), Tainan, 13–16 May 2014, pp 126–138Google Scholar
  14. Jiang M, Cui P, Chen X, Wang F, Zhu W, Yang S (2015a) Social recommendation with cross-domain transferable knowledge. IEEE Trans Knowl Data Eng 27(11):3084–3097Google Scholar
  15. Jiang M, Beutel A, Cui P, Hooi B, Yang S, Faloutsos C (2015c) A general suspiciousness metric for dense blocks in multimodal data. In: The 15th IEEE International Conference on Data Mining (ICDM)Google Scholar
  16. Jiang M, Cui P, Yuan NJ, Xie X, Yang S (2016a) Little is much: bridging cross-platform behaviors through overlapped crowds. In: Proceedings of the thirtieth AAAI conference on artificial intelligence, Phoenix, 12–17 Feb 2016, pp 13–19Google Scholar
  17. Jiang M, Cui P, Faloutsos C (2016b) Suspicious behavior detection: current trends and future directions. Intell Syst IEEE 31(1):31–39CrossRefGoogle Scholar
  18. Jiang M, Cui P, Beutel A, Faloutsos C, Yang S (2016c) Inferring lockstep behavior from connectivity pattern in large graphs. Knowl Inf Syst 48(2):399–428Google Scholar
  19. Koren Y (2008) Factorization meets the neighborhood: a multifaceted collaborative filtering model. In: Proceedings of the 14th ACM SIGKDD international conference on knowledge discovery and data mining, Las Vegas, 24–27 Aug 2008. ACM, New York, pp 426–434. ISBN:978-1-60558-193-4Google Scholar
  20. Koren Y (2010) Collaborative filtering with temporal dynamics. Commun ACM 53(4):89–97CrossRefGoogle Scholar
  21. Koren Y, Bell R, Volinsky C (2009) Matrix factorization techniques for recommender systems. Computer 8:30–37CrossRefGoogle Scholar
  22. Li B, Yang Q, Xue (2009) Can movies and books collaborate? Cross-domain collaborative filtering for sparsity reduction. Hong Kong, China, IJCAI 9:2052–2057. ISBN: 978-1-60558-512-3Google Scholar
  23. Liben-Nowell D, Kleinberg J (2007) The link-prediction problem for social networks. J Am Soc Inf Sci Technol 58(7):1019–1031CrossRefGoogle Scholar
  24. Liu X, Aberer K (2013) Soco: a social network aided context-aware recommender system. In: Proceedings of the 22nd international conference on World Wide Web, Rio de Janeiro, 13–17 May 2013, pp 781–802Google Scholar
  25. Liu NN, Zhao M, Yang Q (2009) Probabilistic latent preference analysis for collaborative filtering. Proceedings of the 18th ACM conference on Information and knowledge management, Hong Kong, 2–6 Nov 2009. ACM, New York, pp 759–766. ISBN:978-1-60558-512-3Google Scholar
  26. Liu Q, Chen E, Xiong H et al (2012) Enhancing collaborative filtering by user interest expansion via personalized ranking. Syst Man Cybern Part B Cybern IEEE Trans 42(1):218–233CrossRefGoogle Scholar
  27. Narang K, Nagar S, Mehta S et al (2013) Discovery and analysis of evolving topical social discussions on unstructured microblogs. In: Proceedings of the 35th European conference on advances in information retrieval, Moscow, 24–27 Mar 2013. Springer, Berlin/Heidelberg, pp 545–556. ISBN:978-3-642-36972-8Google Scholar
  28. Sarwar B, Karypis G, Konstan J et al (2001) Item-based collaborative filtering recommendation algorithms. Proceedings of the 10th international conference on World Wide Web, Hong Kong, 1–5 May 2001. ACM, New York, pp 285–295. ISBN:1-58113-348-0Google Scholar
  29. Schilit B, Adams N, Want R (1994) Context-aware computing applications. Proceedings of the 1994 first workshop on mobile computing systems and applications, 8–9 Dec 1994, pp 85–90Google Scholar
  30. Shmueli G (2010) To explain or to predict? Stat Sci 25(3):289–310. doi:10.1214/10-STS330Google Scholar
  31. Tang J, Hu X, Gao H et al (2013) Exploiting local and global social context for recommendation. Proceedings of the twenty-third international joint conference on artificial Intelligence, Beijing, 3–9 Aug 2013. AAAI Press, pp 2712–2718. ISBN:978-1-57735-633-2Google Scholar
  32. Xu Q, Xiang EW, Yang Q, Du J, Zhong J (2012) Sms spam detection using noncontent features. IEEE Intell Syst 27(6):44–51Google Scholar
  33. Yuan Q, Cong G, Ma Z, Sun A, Thalmann NM (2013) Who, where, when and what: discover spatio-temporal topics for twitter users. In: Proceedings of the 19th ACM SIGKDD international conference on knowledge discovery and data mining, Chicago, 11–14 Aug 2013. ACM, New York, pp 605–613. ISBN:978-1-4503-2174-7Google Scholar
  34. Zhong E, Fan W, Yang Q (2014) User behavior learning and transfer in composite social networks. ACM TKDD 8(1):6Google Scholar

Copyright information

© Springer Science+Business Media LLC 2017

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringUniversity of Notre DameNotre DameUSA

Section editors and affiliations

  • Guandong Xu
    • 1
  • Peng Cui
    • 2
  1. 1.University of Technology SydneySydneyAustralia
  2. 2.Tsinghua UniversityBeijingChina