Data-Driven Modeling and Analysis of Online Social Networks

  • Divyakant Agrawal
  • Bassam Bamieh
  • Ceren Budak
  • Amr El Abbadi
  • Andrew Flanagin
  • Stacy Patterson
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6897)

Abstract

With hundreds of millions of users worldwide, social networks provide incredible opportunities for social connection, learning, political and social change, and individual entertainment and enhancement in a wide variety of forms. In light of these notable outcomes, understanding information diffusion over online social networks is a critical research goal. Because many social interactions currently take place in online networks, we now have have access to unprecedented amounts of information about social interaction. Prior to the advent of such online networks, investigations about social behavior required resource-intensive activities such as random trials, surveys, and manual data collection to gather even small data sets. Now, massive amounts of information about social networks and social interactions are recorded. This wealth of data can allow us to study social interactions on a scale and at a level of detail that has never before been possible.

We present an integrated approach to information diffusion in online social networks focusing on three key problems: (1) Querying and analysis of online social network datasets; (2) Modeling and analysis of social networks; and (3) Analysis of social media and social interactions in the contemporary media environment. The overarching goals are to generate a greater understanding of social interactions in online networks through data analysis, to develop reliable and scalable models that can predict outcomes of these social processes, and ultimately to create applications that can shape the outcome of these processes. We start by developing and refining models of information diffusion based on real-world data sets. We next address the problem of finding influential users in this data-driven framework. It is equally important to identify techniques that can slow or prevent the spread of misinformation, and hence algorithms are explored to address this question. A third interest is the process by which a social group forms opinions about an idea or product, and we therefore describe preliminary approaches to create models that accurately capture the opinion formation process in online social networks. While questions relating to the propagation of a single news item or idea are important, these information campaigns do not exist in isolation. Therefore, our proposed approach also addresses the interplay of the many information diffusion processes that take place simultaneously in a network and the relative importance of different topics or trends over multiple spatial and temporal resolutions.

Keywords

Information propagation Social Networks Data Analysis Sub-modular optimization 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. [AG05]
    Adamic, L.A., Glance, N.: The political blogosphere and the 2004 u.s. election: divided they blog. In: LinkKDD 2005: Proceedings of the 3rd International Workshop on Link Discovery, pp. 36–43 (2005)Google Scholar
  2. [BAEA10]
    Budak, C., Agrawal, D., El Abbadi, A.: Where the blogs tip: connectors, mavens, salesmen and translators of the blogosphere. In: SIGKDD Workshop on Social Media Analytics (2010)Google Scholar
  3. [BAEA11a]
    Budak, C., Agrawal, D., El Abbadi, A.: Limiting the spread of misinformation in social networks. In: Proceedings of the 20th International Conference on World Wide Web, WWW 2011, pp. 665–674 (2011)Google Scholar
  4. [BAEA11b]
    Budak, C., Agrawal, D., El Abbadi, A.: Structural trend analysis for online social networks. In: VLDB 2011 (2011)Google Scholar
  5. [BBCG08]
    Becchetti, L., Boldi, P., Castillo, C., Gionis, A.: Efficient semi-streaming algorithms for local triangle counting in massive graphs. In: KDD 2008: Proceeding of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 16–24. ACM, New York (2008)Google Scholar
  6. [BGMI05]
    Baumes, J., Goldberg, M., Magdon-Ismail, M.: Efficient identification of overlapping communities. In: IEEE International Conference on Intelligence and Security Informatics (ISI), pp. 27–36 (2005)Google Scholar
  7. [BKS07]
    Bharathi, S., Kempe, D., Salek, M.: Competitive influence maximization in social networks. In: Deng, X., Graham, F.C. (eds.) WINE 2007. LNCS, vol. 4858, pp. 306–311. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  8. [CCFC02]
    Charikar, M., Chen, K., Farach-Colton, M.: Finding frequent items in data streams. In: Widmayer, P., Triguero, F., Morales, R., Hennessy, M., Eidenbenz, S., Conejo, R. (eds.) ICALP 2002. LNCS, vol. 2380, pp. 693–703. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  9. [Cla05]
    Clauset, A.: Finding local community structure in networks. Physical Review E (Statistical, Nonlinear, and Soft Matter Physics) 72(2), 026132 (2005)CrossRefGoogle Scholar
  10. [CM05]
    Cormode, G., Muthukrishnan, S.: What’s Hot and What’s Not: Tracking Most Frequent Items Dynamically. ACM Trans. Database Syst. 30(1), 249–278 (2005)CrossRefGoogle Scholar
  11. [CNWvZ07]
    Carnes, T., Nagarajan, C., Wild, S.M., van Zuylen, A.: Maximizing influence in a competitive social network: a follower’s perspective. In: ICEC 2007: Proceedings of the Ninth International Conference on Electronic Commerce, pp. 351–360 (2007)Google Scholar
  12. [CYY09]
    Chen, W., Wang, Y., Yang, S.: Efficient influence maximization in social networks. In: Proceedings of the 15th ACM International Conference on Knowledge Discovery and Data Mining, pp. 199–208 (2009)Google Scholar
  13. [DeG74]
    DeGroot, M.H.: Reaching a consensus. Journal of the American Statistical Association 69, 118–121 (1974)CrossRefMATHGoogle Scholar
  14. [DG08]
    Dean, J., Ghemawat, S.: Mapreduce: simplified data processing on large clusters. ACM Commun. 51(1), 107–113 (2008)CrossRefGoogle Scholar
  15. [DGM06]
    Dubey, P., Garg, R., De Meyer, B.: Competing for customers in a social network. Cowles Foundation Discussion Papers 1591, Cowles Foundation, Yale University (November 2006)Google Scholar
  16. [DLOM02]
    Demaine, E.D., López-Ortiz, A., Munro, J.I.: Frequency estimation of internet packet streams with limited space. In: Möhring, R.H., Raman, R. (eds.) ESA 2002. LNCS, vol. 2461, pp. 348–360. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  17. [DR01]
    Domingos, P., Richardson, M.: Mining the network value of customers. In: Proceedings of the 7th ACM International Conference on Knowledge Discovery and Data Mining, pp. 57–66 (2001)Google Scholar
  18. [Far09]
    Farrell, M.B.: Schwarzenegger tweets about swine flu. so does everyone else. Christian Science Monitor (April 2009)Google Scholar
  19. [FJ99]
    Friedkin, N.E., Johnsen, E.C.: Social influence networks and opinion change. Advances in Group Processes 16, 1–29 (1999)Google Scholar
  20. [Fre56]
    French, J.R.P.: A formal theory of social power. Psychological Review 63, 181–194 (1956)CrossRefGoogle Scholar
  21. [GL08]
    Ghosh, R., Lerman, K.: Community Detection Using a Measure of Global Influence. In: Giles, L., Smith, M., Yen, J., Zhang, H. (eds.) SNAKDD 2008. LNCS, vol. 5498, pp. 20–35. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  22. [Gla02]
    Gladwell, M.: The Tipping Point: How Little Things Can Make a Big Difference. Back Bay Books (January 2002)Google Scholar
  23. [GLM01]
    Goldenberg, J., Libai, B., Muller, E.: Talk of the network: A complex systems look at the underlying process of word-of-mouth. Marketing Letters 12(3), 209–221 (2001)CrossRefGoogle Scholar
  24. [Gra78]
    Granovetter, M.: Threshold models of collective behavior. American Journal of Sociology 83(6), 1420–1443 (1978)CrossRefGoogle Scholar
  25. [Gre07]
    Gregory, S.: An algorithm to find overlapping community structure in networks. In: Kok, J.N., Koronacki, J., Lopez de Mantaras, R., Matwin, S., Mladenič, D., Skowron, A. (eds.) PKDD 2007. LNCS (LNAI), vol. 4702, pp. 91–102. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  26. [Gro09]
    Grossman, L.: Iran protests: Twitter, the medium of the movement. Time (online) (June 2009)Google Scholar
  27. [HK02]
    Hegselmann, R., Krause, U.: Opinion dynamics and bounded confidence models, analysis, and simulation. Journal of Artificial Societies and Social Simulation 5(3) (2002)Google Scholar
  28. [HP09]
    Hughes, A.L., Palen, L.: Twitter adoption and use in mass convergence and emergency events. In: Proceedings of the 6th International Information Systems for Crisis Response and Management Conference (2009)Google Scholar
  29. [HR06]
    Horrigan, J., Rainie, L.: When facing a tough decision, 60 million americans now seek the internet’s help: The internet’s growing role in life’s major moments (2006), http://pewresearch.org/obdeck/?ObDeckID=19 (retrieved October 13, 2006)
  30. [KKT03]
    Kempe, D., Kleinberg, J.M., Tardos, É.: Maximizing the spread of influence through a social network. In: Proceedings of the Ninth ACM International Conference on Knowledge Discovery and Data Mining, pp. 137–146 (2003)Google Scholar
  31. [KOW08]
    Kostka, J., Oswald, Y.A., Wattenhofer, R.: Word of Mouth: Rumor Dissemination in Social Networks. In: 15th International Colloquium on Structural Information and Communication Complexity (SIROCCO) (June 2008)Google Scholar
  32. [KSNM09]
    Kimura, M., Saito, K., Nakano, R., Motoda, H.: Finding Influential Nodes in a Social Network from Information Diffusion Data. In: Social Computing and Behavioral Modeling. Springer, US (2009)Google Scholar
  33. [Leh75]
    Lehrer, K.: Social consensus and rational agnoiology. Synthese 31(1), 141–160 (1975)CrossRefGoogle Scholar
  34. [LKG+07]
    Leskovec, J., Krause, A., Guestrin, C., Faloutsos, C., VanBriesen, J., Glance, N.: Cost-effective outbreak detection in networks. In: Proceedings of the 13th ACM International Conference on Knowledge Discovery and Data Mining, pp. 420–429 (2007)Google Scholar
  35. [MAE06]
    Metwally, A., Agrawal, D., El Abbadi, A.: An integrated efficient solution for computing frequent and top-k elements in data streams. ACM Trans. Database Syst. 31(3), 1095–1133 (2006)CrossRefGoogle Scholar
  36. [MM02]
    Manku, G.S., Motwani, R.: Approximate frequency counts over data streams. In: Proc. 28th Int. Conf. on Very Large Data Bases, pp. 346–357 (2002)Google Scholar
  37. [MMG+07]
    Mislove, A., Marcon, M., Gummadi, K.P., Druschel, P., Bhattacharjee, B.: Measurement and analysis of online social networks. In: Proc. 7th ACM SIGCOMM Conf. on Internet Measurement, pp. 29–42 (2007)Google Scholar
  38. [New03]
    Newman, M.E.J.: Fast algorithm for detecting community structure in networks. Physical Review E 69 (September 2003)Google Scholar
  39. [New06]
    Newman, M.E.J.: Modularity and community structure in networks. Proceedings of the National Academy of Sciences 103(23), 8577–8582 (2006)CrossRefGoogle Scholar
  40. [PB10]
    Patterson, S., Bamieh, B.: Interaction-driven opinion dynamics in online social networks. In: SIGKDD Workshop on Social Media Analytics (2010)Google Scholar
  41. [PBE10]
    Patterson, S., Bamieh, B., El Abbadi, A.: Convergence rates of distributed average consensus with stochastic link failures. IEEE Transactions on Automatic Control 55(4), 880–892 (2010)MathSciNetCrossRefGoogle Scholar
  42. [PSL90]
    Pothen, A., Simon, H.D., Liou, K.-P.: Partitioning sparse matrices with eigenvectors of graphs. SIAM J. Matrix Anal. Appl. 11(3), 430–452 (1990)MathSciNetCrossRefMATHGoogle Scholar
  43. [RD02]
    Richardson, M., Domingos, P.: Mining knowledge-sharing sites for viral marketing. In: Proceedings of the 8th ACM International Conference on Knowledge Discovery and Data Mining, pp. 61–70 (2002)Google Scholar
  44. [WBS+09]
    Wilson, C., Boe, B., Sala, A., Puttaswamy, K.P.N., Zhao, B.Y.: User interactions in social networks and their implications. In: Proc. 4th ACM European Conference on Computer Systems, pp. 205–218 (2009)Google Scholar
  45. [WCSX10]
    Wang, Y., Cong, G., Song, G., Xie, K.: Community-based greedy algorithm for mining top-k influential nodes in mobile social networks. In: Proceedings of the 16th ACM International Conference on Knowledge Discovery and Data Mining (2010)Google Scholar
  46. [WF94]
    Wasserman, S., Faust, K.: Social Network Analysis. Cambridge University Press, Cambridge (1994)CrossRefMATHGoogle Scholar
  47. [ZWZ07]
    Zhang, S., Wang, R.S., Zhang, X.S.: Identification of overlapping community structure in complex networks using fuzzy c-means clustering. Physica A: Statistical Mechanics and its Applications 374(1), 483–490 (2007)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Divyakant Agrawal
    • 1
  • Bassam Bamieh
    • 2
  • Ceren Budak
    • 1
  • Amr El Abbadi
    • 1
  • Andrew Flanagin
    • 3
  • Stacy Patterson
    • 2
  1. 1.Department of Computer ScienceUniversity of CaliforniaSanta BarbaraUSA
  2. 2.Department of Mechanical EngineeringUniversity of CaliforniaSanta BarbaraUSA
  3. 3.Department of CommunicationUniversity of CaliforniaSanta BarbaraUSA

Personalised recommendations