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Scalable Information Flow Mining in Networks

  • Karthik Subbian
  • Chidananda Sridhar
  • Charu C. Aggarwal
  • Jaideep Srivastava
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8726)

Abstract

The problem of understanding user activities and their patterns of communication is extremely important in social and collaboration networks. This can be achieved by tracking the dominant content flow trends and their interactions between users in the network. Our approach tracks all possible paths of information flow using its network structure, content propagated and the time of propagation. We also show that the complexity class of this problem is #P-complete. Because most social networks have many activities and interactions, it is inevitable the proposed method will be computationally intensive. Therefore, we propose an efficient method for mining information flow patterns, especially in large networks, using distributed vertex-centric computational models. We use the Gather-Apply-Scatter (GAS) paradigm to implement our approach. We experimentally show that our approach achieves over three orders of magnitude advantage over the state-of-the-art, with an increasing advantage with a greater number of cores. We also study the effectiveness of the discovered content flow patterns by using it in the context of an influence analysis application.

Keywords

Information Flow Mining Vertex-centric models Influence Analysis Network-centric approach Scalable Influence Analysis 

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References

  1. 1.
    Adar, E., Adamic, L.: Tracking information epidemics in blogspace. In: Web Intelligence, pp. 207–214 (2005)Google Scholar
  2. 2.
    Aggarwal, C., Subbian, K.: Event detection in social streams. In: SDM, pp. 624–635 (2012)Google Scholar
  3. 3.
    Aggarwal, C., Subbian, K.: Evolutionary network analysis: A survey. ACM Comput. Surv. 47(1), 10 (2014)CrossRefGoogle Scholar
  4. 4.
    Bonchi, F., De Francisci Morales, G., Gionis, A., Ukkonen, A.: Activity preserving graph simplification. Data Mining and Knowledge Discovery 27(3), 321–343 (2013)CrossRefzbMATHMathSciNetGoogle Scholar
  5. 5.
    Chen, W., Wang, C., Wang, Y.: Scalable influence maximization for prevalent viral marketing in large-scale social networks. In: KDD, pp. 1029–1038 (2010)Google Scholar
  6. 6.
    Chen, W., Wang, Y., Yang, S.: Efficient influence maximization in social networks. In: KDD, pp. 199–208 (2009)Google Scholar
  7. 7.
    Galuba, W., Aberer, K., Chakraborty, D., Despotovic, Z., Kellerer, W.: Outtweeting the twitterers-predicting information cascades in microblogs. In: WOSN (2010)Google Scholar
  8. 8.
    Gonzalez, J., Low, Y., Gu, H., Bickson, D., Guestrin, C.: Powergraph: Distributed graph-parallel computation on natural graphs. In: USENIX (2012)Google Scholar
  9. 9.
    Kempe, D., Kleinberg, J.M., Tardos, E.: Maximizing the spread of influence through a social network. In: KDD, pp. 137–146 (2003)Google Scholar
  10. 10.
    Kim, Y.A., Przytycki, J.H., Wuchty, S., Przytycka, T.M.: Modeling information flow in biological networks. Physical Biology 8(3), 035012 (2011)Google Scholar
  11. 11.
    Lerman, K., Ghosh, R.: Information contagion: An empirical study of the spread of news on digg and twitter social networks. In: ICWSM (2010)Google Scholar
  12. 12.
    Leskovec, J., Backstrom, L., Kleinberg, J.M.: Meme-tracking and the dynamics of the news cycle. In: KDD, pp. 497–506 (2009)Google Scholar
  13. 13.
    Leskovec, J., McGlohon, M., Faloutsos, C., Glance, N.S., Hurst, M.: Cascading behavior in large blog graphs. In: SDM (2007)Google Scholar
  14. 14.
    Low, Y., Gonzalez, J., Kyrola, A., Bickson, D., Guestrin, C., Hellerstein, J.M.: Graphlab: A new framework for parallel machine learning. arXiv:1006.4990 (2010)Google Scholar
  15. 15.
    Mathioudakis, M., Bonchi, F., Castillo, C., Gionis, A., Ukkonen, A.: Sparsification of influence networks. In: KDD, pp. 529–537 (2011)Google Scholar
  16. 16.
    Myers, S.A., Zhu, C., Leskovec, J.: Information diffusion and external influence in networks. In: KDD, pp. 33–41 (2012)Google Scholar
  17. 17.
    Pei, J., Pinto, H., Chen, Q., Han, J., Mortazavi-Asl, B., Dayal, U., Hsu, M.-C.: Prefixspan: Mining sequential patterns efficiently by prefix-projected pattern growth. In: ICDE, pp. 215–215 (2001)Google Scholar
  18. 18.
    Rodriguez, M.G., Leskovec, J., Krause, A.: Inferring networks of diffusion and influence. In: KDD, pp. 1019–1028 (2010)Google Scholar
  19. 19.
    Rodriguez, M.G., Leskovec, J., Schölkopf, B.: Structure and dynamics of information pathways in online media. In: WSDM, pp. 23–32 (2013)Google Scholar
  20. 20.
    Subbian, K., Aggarwal, C., Srivastava, J.: Content-centric flow mining for influence analysis in social streams. In: CIKM (2013)Google Scholar
  21. 21.
    Subbian, K., Melville, P.: Supervised rank aggregation for predicting influencers in twitter. In: SocialCom, pp. 661–665 (2011)Google Scholar
  22. 22.
    Subbian, K., Sharma, D., Wen, Z., Srivastava, J.: Social capital: the power of influencers in networks. In: AAMAS, pp. 1243–1244 (2013)Google Scholar
  23. 23.
    Wang, X., Zhai, C., Hu, X., Sproat, R.: Mining correlated bursty topic patterns from coordinated text streams. In: KDD, pp. 784–793 (2007)Google Scholar
  24. 24.
    Weng, L., Flammini, A., Vespignani, A., Menczer, F.: Competition among memes in a world with limited attention. Scientific Reports 2 (2012)Google Scholar
  25. 25.
    Yang, G.: The complexity of mining maximal frequent itemsets and maximal frequent patterns. In: SIGKDD, pp. 344–353 (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Karthik Subbian
    • 1
  • Chidananda Sridhar
    • 1
  • Charu C. Aggarwal
    • 2
  • Jaideep Srivastava
    • 1
  1. 1.University of MinnesotaMinneapolisUSA
  2. 2.IBM T. J. Watson Research CenterYorktown HeightsUSA

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