ABSTRACT
Users in online social networks play a variety of social roles and statuses. For example, users in Twitter can be represented as advertiser, content contributor, information receiver, etc; users in Linkedin can be in different professional roles, such as engineer, salesperson and recruiter. Previous research work mainly focuses on using categorical and textual information to predict the attributes of users. However, it cannot be applied to a large number of users in real social networks, since much of such information is missing, outdated and non-standard. In this paper, we investigate the social roles and statuses that people act in online social networks in the perspective of network structures, since the uniqueness of social networks is connecting people. We quantitatively analyze a number of key social principles and theories that correlate with social roles and statuses. We systematically study how the network characteristics reflect the social situations of users in an online society. We discover patterns of homophily, the tendency of users to connect with users with similar social roles and statuses. In addition, we observe that different factors in social theories influence the social role/status of an individual user to various extent, since these social principles represent different aspects of the network. We then introduce an optimization framework based on Factor Conditioning Symmetry, and we propose a probabilistic model to integrate the optimization framework on local structural information as well as network influence to infer the unknown social roles and statuses of online users. We will present experiment results to show the effectiveness of the inference.
- C. C. Aggarwal. Social Network Data Analytics. Springer Publishing Company, Incorporated, 1st edition, 2011. Google ScholarCross Ref
- C. C. Aggarwal, Y. Zhao, and P. S. Yu. On text clustering with side information. ICDE '12, pages 894--904. Google ScholarDigital Library
- C. C. Aggarwal, Y. Zhao, and P. S. Yu. Outlier detection in graph streams. ICDE '11, pages 399--409, 2011. Google ScholarDigital Library
- L. Backstrom and J. Leskovec. Supervised random walks: predicting and recommending links in social networks. In WSDM '11, pages 635--644, New York, NY, USA. Google ScholarDigital Library
- R. Burt. Structural holes: The social structure of competition. Harvard University Press, 1995.Google Scholar
- E. David and K. Jon. Networks, Crowds, and Markets: Reasoning About a Highly Connected World. Cambridge University Press, New York, NY, USA, 2010. Google ScholarDigital Library
- M. De Choudhury, W. A. Mason, J. M. Hofman, and D. J. Watts. Inferring relevant social networks from interpersonal communication. In WWW '10, pages 301--310, New York, NY, USA. Google ScholarDigital Library
- M. Granovetter. Economic Action and Social Structure: The Problem of Embeddedness. The American Journal of Sociology, 91(3):481--510, 1985.Google ScholarCross Ref
- S. Guo, M. Wang, and J. Leskovec. The role of social networks in online shopping: information passing, price of trust, and consumer choice. In EC '11, pages 157--166, New York, NY, USA. Google ScholarDigital Library
- K. Henderson, B. Gallagher, T. Eliassi-Rad, H. Tong, S. Basu, L. Akoglu, D. Koutra, C. Faloutsos, and L. Li. Rolx: structural role extraction & mining in large graphs. KDD '12, pages 1231--1239. ACM, 2012. Google ScholarDigital Library
- X. Hu and H. Liu. Social status and role analysis of palin's email network. In WWW '12 Companion, pages 531--532, New York, NY, USA. Google ScholarDigital Library
- D. Koller and N. Friedman. Probabilistic Graphical Models: Principles and Techniques - Adaptive Computation and Machine Learning. The MIT Press, 2009. Google ScholarDigital Library
- G. Kossinets and D. Watts. Empirical analysis of an evolving social network. Science, 311(5757):88--90, 2006.Google ScholarCross Ref
- J. Leskovec, L. A. Adamic, and B. A. Huberman. The dynamics of viral marketing. ACM Trans. Web, 1(1), May 2007. Google ScholarDigital Library
- A. Leuski. Email is a stage: discovering people roles from email archives. In SIGIR '04, pages 502--503, New York, NY, USA. Google ScholarDigital Library
- D. Liben-Nowell and J. Kleinberg. The link prediction problem for social networks. In CIKM '03, pages 556--559, New York, NY, USA. Google ScholarDigital Library
- H.-T. Lin, C.-J. Lin, and R. C. Weng. A note on platt's probabilistic outputs for support vector machines. Mach. Learn., 68(3):267--276, Oct. 2007. Google ScholarDigital Library
- A. McCallum, X. Wang, and A. Corrada-Emmanuel. Topic and role discovery in social networks with experiments on enron and academic email. J. Artif. Int. Res., 30(1):249--272, Oct. 2007. Google ScholarDigital Library
- M. McPherson, L. S. Lovin, and J. M. Cook. Birds of a Feather: Homophily in Social Networks. Annual Review of Sociology, 27(1):415--444, 2001.Google ScholarCross Ref
- A. Mislove, B. Viswanath, K. P. Gummadi, and P. Druschel. You are who you know: inferring user profiles in online social networks. In WSDM '10, pages 251--260, New York, NY, USA. Google ScholarDigital Library
- S. A. Myers and J. Leskovec. On the convexity of latent social network inference. In J. D. Lafferty, C. K. I. Williams, J. Shawe-Taylor, R. S. Zemel, and A. Culotta, editors, NIPS '10, pages 1741--1749. Curran Associates, Inc., 2010.Google Scholar
- P. Singla and M. Richardson. Yes, there is a correlation: - from social networks to personal behavior on the web. In WWW '08, pages 655--664, New York, NY, USA. Google ScholarDigital Library
- C. Tan, L. Lee, J. Tang, L. Jiang, M. Zhou, and P. Li. User-level sentiment analysis incorporating social networks. In KDD '11, pages 1397--1405, New York, NY, USA. Google ScholarDigital Library
- J. Tang, T. Lou, and J. Kleinberg. Inferring social ties across heterogenous networks. In WSDM '12, pages 743--752, New York, NY, USA. Google ScholarDigital Library
- J. Tang, J. Sun, C. Wang, and Z. Yang. Social influence analysis in large-scale networks. In KDD '09, pages 807--816, New York, NY, USA. Google ScholarDigital Library
- H. Tischler. Introduction to sociology. Wadsworth Publishing Company, 2010.Google Scholar
- G. Wang, Y. Zhao, X. Shi, and P. S. Yu. Magnet community identification on social networks. KDD '12, pages 588--596. Google ScholarDigital Library
- H. T. Welser, D. Cosley, G. Kossinets, A. Lin, F. Dokshin, G. Gay, and M. Smith. Finding social roles in wikipedia. In iConference '11, pages 122--129, New York, NY, USA. Google ScholarDigital Library
- S. Wu, J. M. Hofman, W. A. Mason, and D. J. Watts. Who says what to whom on twitter. In WWW '11, pages 705--714, New York, NY, USA. Google ScholarDigital Library
- T.-F. Wu, C.-J. Lin, and R. C. Weng. Probability estimates for multi-class classification by pairwise coupling. J. Mach. Learn. Res., 5:975--1005, Dec. 2004. Google ScholarDigital Library
- Y. Zhao, C. C. Aggarwal, and P. S. Yu. On graph stream clustering with side information. In SDM, 2013.Google ScholarCross Ref
- Y. Zhao, X. Kong, and P. S. Yu. Positive and unlabeled learning for graph classification. ICDM '11, pages 962--971. Google ScholarDigital Library
- Y. Zhao, N. Sundaresan, Z. Shen, and P. S. Yu. Anatomy of a web-scale resale market: a data mining approach. WWW '13, pages 1533--1544. Google ScholarDigital Library
- E. Zheleva and L. Getoor. To join or not to join: the illusion of privacy in social networks with mixed public and private user profiles. In WWW '09, pages 531--540, New York, NY, USA. Google ScholarDigital Library
Index Terms
- Inferring social roles and statuses in social networks
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