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Influential Users in Social Networks

  • Dimitrios Vogiatzis
Part of the Studies in Computational Intelligence book series (SCI, volume 418)

Abstract

A study of the influential users in online social networks is the focus of this work. Social networks expand both in terms of membership and diversity. User driven content creation is growing, and yet this information potential remains largely untapped. Future search engines focusing in social networks should take into account both the content and the structural properties of the nodes.Whereas a social network bears a superficial similarity to the Web, it is different in the sense that it connects primarily individuals rather than pages of content. Not all individuals are equally important for any given task, therefore the influential ones should be detected, in that vein we review facets of influence in social networks.

Keywords

Social Network Online Social Network Preferential Attachment Expert User Linear Threshold 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Albert, R., Barabási, A.L.: Topology of complex networks: Local events and universality. Physical Review Letters 85(24) (2000)Google Scholar
  2. 2.
    Amatriain, X., Lathia, N., Pujol, J.M., Kwak, H., Oliver, N.: The wisdom of the few: a collaborative filtering approach based on expert opinions from the web. In: Proceedings of the 32nd International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR, pp. 532–539. ACM, New York (2009)CrossRefGoogle Scholar
  3. 3.
    Balog, K., Bogers, T., Azzopardi, L., de Rijke, M., van den Bosch, A.: Broad expertise retrieval in sparse data environments. In: Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR, pp. 551–558. ACM, New York (2007)CrossRefGoogle Scholar
  4. 4.
    Bradham, D.C.: Crowdsourcing as a Model for Problem Solving: An Introduction and Cases. Covergence: The International Journal of Research into New Media Technologies 14, 75–90 (2008)CrossRefGoogle Scholar
  5. 5.
    Cao, T., Wu, X., Hu, T.X., Wang, S.: Active Learning of Model Parameters for Influence Maximization. In: Gunopulos, D., Hofmann, T., Malerba, D., Vazirgiannis, M. (eds.) ECML PKDD 2011. LNCS, vol. 6911, pp. 280–295. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  6. 6.
    Cho, J., Kwon, K., Park, Y.: Collaborative filtering using dual information sources. IEEE Intelligent Systems 22, 30–38 (2007)CrossRefGoogle Scholar
  7. 7.
    Davenport, T., Prusak, L.: Working Knowledge. Harvard Business Press (1998)Google Scholar
  8. 8.
    Dima, M., Vogiatzis, D., Paliouras, G.: Expert based prediction of user preferences. In: 5th International Workshop on Semantic Media Adaptation and Personalization, SMAP (2010)Google Scholar
  9. 9.
    Dom, B., Eiron, I., Cozzi, A., Zhang, Y.: Graph-based ranking algorithms for e-mail expertise analysis. In: Proceedings of the 8th ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery, DMKD, pp. 42–48. ACM, New York (2003)CrossRefGoogle Scholar
  10. 10.
    Domingos, P., Richardson, M.: Mining the network value of customers. In: Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2001, pp. 57–66. ACM, New York (2001)CrossRefGoogle Scholar
  11. 11.
    Dorogotsev, S., Mendes, J.F.F.: Scaling behaviour of developing and decaying networks. Europhys. Lett. 52(33) (2000)Google Scholar
  12. 12.
    Granger, C.: Investigating causal relations by econometric models and cross-spectral methods. Econometrica 37(3), 424–438 (1969)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Huberman, B.A., Romero, D.M., Wu, F.: Crowdsourcing, attention and productivity. Journal of Information Science 35(6), 758–765 (2009)CrossRefGoogle Scholar
  14. 14.
    Hubermand, B.A., Romero, D.M., Wu, F.: Social networks that matter: Twitter under the microscope. arXiv:0812.1045v1 (2008)Google Scholar
  15. 15.
    Kautz, H., Selman, B., Shah, M.: Referral web: combining social networks and collaborative filtering. Commun. ACM 40, 63–65 (1997)CrossRefGoogle Scholar
  16. 16.
    Kautz, H., Selman, B., Shah, M.: The Hidden Web. AI Magazine 18(2), 27–36 (1997)Google Scholar
  17. 17.
    Kempe, D., Kleinberg, J., Tardos, E.: Maximizing the spread of influence through a social network. In: Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 137–146. ACM, New York (2003)CrossRefGoogle Scholar
  18. 18.
    Kitsak, M., Gallos, L.K., Havlin, S., Liljeros, F., Muchnik, L., Stanley, H.E., Makse, H.A.: Identifying influential spreaders in complex networks. Nature Physics 6, 888–903 (2010)CrossRefGoogle Scholar
  19. 19.
    Kitsak, M., Gallos, L.K., Havlin, S., Liljeros, F., Muchnik, L., Stanley, H.E., Makse, H.A.: Identifying influential spreaders in complex networks. arXiv:1001.5285 (2010)Google Scholar
  20. 20.
    Kittur, A., Chi, E., Pendelton, B.A., Suh, B., Mytkowicz, T.: Power of the Few vs. Wisdom of the Crowd: Wikipedia and the Rise of the Bourgeoisie. In: 25th Annual ACM Conference on Human Factors in Computing Systems, CHI (2007)Google Scholar
  21. 21.
    Krapivsky, P., Redner, S., Leyvraz, F.: Connectivity of Growing Random Networks. Physical Review Letters 85(21) (2000)Google Scholar
  22. 22.
    Leskovec, J., Singh, A., Kleinberg, J.M.: Patterns of influence in a recommedation network. In: Proceedings of the 10th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PKDD (2006)Google Scholar
  23. 23.
    Mislove, A., Marcon, M., Gummadi, K.P., Druschel, P., Bhattacharjee, B.: Measurement and analysis of online social networks. In: Proceedings of the 7th ACM SIGCOMM Conference on Internet Measurement, IMC, pp. 29–42. ACM, New York (2007)CrossRefGoogle Scholar
  24. 24.
    O’Donovan, J., Smyth, B.: Trust in recommender systems. In: Proceedings of the 10th International Conference on Intelligent User Interfaces, IUI 2005, pp. 167–174. ACM, New York (2005)CrossRefGoogle Scholar
  25. 25.
    Réka, A., László, B.A.: Statistical mechanics of complex networks. Rev. Mod. Phys. 74(1), 47–97 (2002)CrossRefGoogle Scholar
  26. 26.
    Sabater, J., Sierra, C.: Review on computational trust and reputation models. Artif. Intell. Rev. 24, 33–60 (2005)zbMATHCrossRefGoogle Scholar
  27. 27.
    Santiago, A., Benito, R.: An extended formalism for preferential attachment in hetereogenous complex networks. EPL 82 (5) (2008)Google Scholar
  28. 28.
    Surowiecki, J.: The wisdom of crowds. Anchow (2005)Google Scholar
  29. 29.
    Yimam, D., Kobsa, A.: Expert finding systems for organisations: Problem and domain analysis and the demoir approach. Journal of Organizational Computing and Electronic Commerce 13(1), 1–24 (2003)CrossRefGoogle Scholar
  30. 30.
    Zhang, J., Ackerman, M.S.: Searching for expertise in social networks: a simulation of potential strategies. In: Proceedings of the 2005 International ACM SIGGROUP Conference on Supporting Group Work, pp. 71–80. ACM, New York (2005)CrossRefGoogle Scholar
  31. 31.
    Zhang, J., Ackerman, M.S., Adamic, L.: Expertise networks in online communities: structure and algorithms. In: Proceedings of the 16th International Conference on World Wide Web, WWW, pp. 221–230. ACM, New York (2007)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Institute of Informatics and TelecommunicationsNCSR “D” and The American College of GreeceAthensGreece

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