skip to main content
10.1145/2556195.2556247acmconferencesArticle/Chapter ViewAbstractPublication PageswsdmConference Proceedingsconference-collections
research-article

Prediction in a microblog hybrid network using bonacich potential

Published:24 February 2014Publication History

ABSTRACT

Microblogs such as Twitter support a rich variety of user interactions using hashtags, urls, retweets and mentions. Microblogs are an exemplar of a hybrid network; there is an explicit network of followers, as well as an implicit network of users who retweet other users, and users who mention other users. These networks are important proxies for influence. In this paper, we develop a comprehensive behavioral model of an individual user and her interactions in the hybrid network. We choose a focal user and predict those users who will be influenced by her, and will retweet and/or mention the focal user, in the near future. We define a potential function, based on a hybrid network, which reflects the likelihood of a candidate user being influenced by, and having a specific type of link to, a focal user, in the future. We show that the potential function based prediction model converges to the Bonacich centrality metric. We develop a fast unsupervised solution which approximates the future hybrid network and the future Bonacich potential. We perform an extensive evaluation over a microblog network and a stream of tweets from Twitter. Our solution outperforms several baseline methods including ones based on singular value decomposition (SVD) and a supervised Ranking SVM.

References

  1. E. Bakshy, J. M. Hofman, W. A. Mason, and D. J. Watts. Everyone's an influencer: quantifying influence on twitter. In WSDM, 2011. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. P. Bonacich. Power and centrality: A family of measures. The American Journal of Sociology, 92(5):1170--1182, 1987.Google ScholarGoogle ScholarCross RefCross Ref
  3. D. Centola. The spread of behavior in an online social network experiment. Science, 329(5996):1194--1197, 2010.Google ScholarGoogle ScholarCross RefCross Ref
  4. M. Cha, H. Haddadi, F. Benevenuto, and K. Gummadi. Measuring user influence in twitter: The million follower fallacy. In ICWSM, 2010.Google ScholarGoogle ScholarCross RefCross Ref
  5. W. Chen, C. Wang, and Y. Wang. Scalable influence maximization for prevalent viral marketing in large-scale social networks. In SIGKDD, 2010. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. W. L. Ferrar. Finite matrices. Oxford Univ. Press, 1951.Google ScholarGoogle Scholar
  7. M. Gjoka, M. Kurant, C. T. Butts, and A. Markopoulou. Walking in facebook: a case study of unbiased sampling of osns. In INFOCOM, 2010. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. J. Goldenberg, B. Libai, and E. Muller. Talk of the network: A complex systems look at the underlying process of word-of-mouth. Marketing Letters, 12(3):211--223, 2001.Google ScholarGoogle ScholarCross RefCross Ref
  9. M. Gomez-Rodriguez, J. Leskovec, and A. Krause. Inferring networks of diffusion and influence. ACM Trans. Knowl. Discov. Data, 5(4):21:1--21:37, Feb. 2012. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. W. Hersh, C. Buckley, T. J. Leone, and D. Hickam. Ohsumed: an interactive retrieval evaluation and new large test collection for research. In SIGIR, 1994. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. L. Hong, A. S. Doumith, and B. D. Davison. Co-factorization machines: modeling user interests and predicting individual decisions in twitter. In WSDM, 2013. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. T. Ito, M. Shimbo, T. Kudo, and Y. Matsumoto. Application of kernels to link analysis. In SIGKDD, 2005. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. T. Joachims. Optimizing search engines using clickthrough data. In SIGKDD, 2002. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. L. Katz. A new status index derived from sociometric analysis. Psychometrika, 18:39--40, 1953.Google ScholarGoogle ScholarCross RefCross Ref
  15. D. Kempe, J. Kleinberg, and E. Tardos. Maximizing the spread of influence through a social network. In SIGKDD, 2003. Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. Y. Koren, R. Bell, and C. Volinsky. Matrix factorization techniques for recommender systems. Computer, 42(8):30--37, Aug. 2009. Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. J. Kunegis and A. Lommatzsch. Learning spectral graph transformations for link prediction. In ICML, 2009. Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. J. Leskovec and C. Faloutsos. Sampling from large graphs. In SIGKDD, 2006. Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. D. Liben-Nowell and J. Kleinberg. The link prediction problem for social networks. In CIKM, 2003. Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. C. Manning, P. Raghavan, and H. Schutze. Introduction to Information Retrieval. Cambridge University Press, 2008. Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. W. Pan, N. Aharony, and A. Pentland. Composite social network for predicting mobile apps installation. In AAAI, 2011.Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. H.-K. Peng, J. Zhu, D. Piao, R. Yan, and Y. Zhang. Retweet modeling using conditional random fields. In ICDMW, 2011. Google ScholarGoogle ScholarDigital LibraryDigital Library
  23. K. Subbian and P. Melville. Supervised rank aggregation for predicting influencers in twitter. In SocialCom/PASSAT, pages 661--665, 2011.Google ScholarGoogle ScholarCross RefCross Ref
  24. J. Tang, J. Sun, C. Wang, and Z. Yang. Social influence analysis in large-scale networks. In SIGKDD, 2009. Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. B. Taskar, M.-F. Wong, P. Abbeel, and D. Koller. Link prediction in relational data. In NIPS, 2003.Google ScholarGoogle ScholarDigital LibraryDigital Library
  26. C. Wang, V. Satuluri, and S. Parthasarathy. Local probabilistic models for link prediction. In ICDM '07: Proceedings of the 2007 Seventh IEEE International Conference on Data Mining, pages 322--331, 2007. Google ScholarGoogle ScholarDigital LibraryDigital Library
  27. S. Wu, L. Raschid, and W. Rand. Future link prediction in the blogosphere for recommendation. In ICWSM '11: Proceedings of the International Conference on Weblogs and Social Media, 2011.Google ScholarGoogle ScholarCross RefCross Ref
  28. Z. Yang, J. Guo, K. Cai, J. Tang, J. Li, L. Zhang, and Z. Su. Understanding retweeting behaviors in social networks. In CIKM '10: Proceedings of the 19th ACM International Conference on Information and Knowledge Management, pages 1633--1636, 2010. Google ScholarGoogle ScholarDigital LibraryDigital Library

Index Terms

  1. Prediction in a microblog hybrid network using bonacich potential

    Recommendations

    Comments

    Login options

    Check if you have access through your login credentials or your institution to get full access on this article.

    Sign in
    • Published in

      cover image ACM Conferences
      WSDM '14: Proceedings of the 7th ACM international conference on Web search and data mining
      February 2014
      712 pages
      ISBN:9781450323512
      DOI:10.1145/2556195

      Copyright © 2014 ACM

      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 24 February 2014

      Permissions

      Request permissions about this article.

      Request Permissions

      Check for updates

      Qualifiers

      • research-article

      Acceptance Rates

      WSDM '14 Paper Acceptance Rate64of355submissions,18%Overall Acceptance Rate498of2,863submissions,17%

    PDF Format

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader