Survey of Influential User Identification Techniques in Online Social Networks

Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 235)


Online social networks became a remarkable development with wonderful social as well as economic impact within the last decade. Currently the most famous online social network, Facebook, counts more than one billion monthly active users across the globe. Therefore, online social networks attract a great deal of attention among practitioners as well as research communities. Taken together with the huge value of information that online social networks hold, numerous online social networks have been consequently valued at billions of dollars. Hence, a combination of this technical and social phenomenon has evolved worldwide with increasing socioeconomic impact. Online social networks can play important role in viral marketing techniques, due to their power in increasing the functioning of web search, recommendations in various filtering systems, scattering a technology (product) very quickly in the market. In online social networks, among all nodes, it is interesting and important to identify a node which can affect the behaviour of their neighbours; we call such node as Influential node. The main objective of this paper is to provide an overview of various techniques for Influential User identification. The paper also includes some techniques that are based on structural properties of online social networks and those techniques based on content published by the users of social network.


Online social networks Influential user Content Active user Viral marketing 


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  1. 1.
    Aggarwal, C.C.: Social Network Data Analytics, pp. 1–14. Springer Science Business Media, LLC (2011)CrossRefMATHGoogle Scholar
  2. 2.
    Richardson, M., Domingos, P.: Mining knowledge-sharing sites for viral marketing. In: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 61–70. ACM Press, New York (2002)Google Scholar
  3. 3.
    Coffman, T., Marcus, S.: Dynamic Classification of groups through social network analysis and HMMs. In: Proceedings of IEEE Aerospace Conference (2004)Google Scholar
  4. 4.
    Keller, E., Berry, J.: One American in ten tells the other nine how to vote, where to eat and, what to buy. They are The Influential. The Free Press (2003)Google Scholar
  5. 5.
    Wasserman, S., Faust, K.: Social Network Analysis: Methods and Applications. Cambridge University Press, New York (1994)CrossRefGoogle Scholar
  6. 6.
    Yang, W.S., Dia, J.B., Cheng, H., Lin, H.T.: Mining Social Networks for Targeted Advertising. In: Proceedings of the 39th Hawaii International Conference on Systems Science, vol. 6, p. 137a. IEEE Computer Society (2006)Google Scholar
  7. 7.
    Kiss, C., Bichler, M.: Decision Support Systems 46, 233–253 (2008)CrossRefGoogle Scholar
  8. 8.
    Beauchamp, M.: An improved index of centrality. Behavioural Science 10, 161–163 (1965)CrossRefGoogle Scholar
  9. 9.
    Hakimi, S.: Optimum locations of switching centers and the absolute centers and medians of a graph. Operations Research 12 (1965)Google Scholar
  10. 10.
    Sabidussi, G.: The centrality index of a graph. Psychometrika 31, 581–603 (1966)MathSciNetCrossRefMATHGoogle Scholar
  11. 11.
    Freemann, L.C.: A set of measures of centrality based on betweenness. Sociometry 40, 35–41 (1977)CrossRefGoogle Scholar
  12. 12.
    Brin, S., Page, L.: The anatomy of a large scale hyper textual web search engine. In: WWW Conference, Australia (1998)Google Scholar
  13. 13.
    Newman, M.E.J.: Analysis of weighted networks. Physical Review E 70Google Scholar
  14. 14.
    Kleinberg, J.: Auth. sources in hyperlinked environment. In: CM-SIAM Symposium on Discrete Algorithms (1998)Google Scholar
  15. 15.
    Singh, S., Mishra, N., Sharma, S.: Survey of Various Techniques for Determining Influential Users in Social Networks. In: IEEE International Conference on ETCCN, India, pp. 398–403 (2013)Google Scholar
  16. 16.
    Granovetter, M.: Threshold models of collective behaviour. American Journal of Sociology 83(6), 1420–1443 (1978)CrossRefGoogle Scholar
  17. 17.
    Goldenberg, J., Libai, B., Muller, E.: Talk of the network: A complex systems look at the underlying process of word-of-mouth. Marketing Letters, 211–223 (August 2001)Google Scholar
  18. 18.
    Kempel, D., Kleinberg, J., Tardos, E.: Maximizing the spread of influence through a social network. In: ACM SIGKDD, pp. 137–146 (2003)Google Scholar
  19. 19.
    Wang, Y., Cong, G., Song, G., Xie, K.: Community-based Greedy Algorithm for Mining Top-K Influential Nodes in Mobile Social Networks. In: KDD 2010, Washington, July 25-28 (2010)Google Scholar
  20. 20.
    Liu, L., Tang, J., Han, J., Jiang, M., Yang, S.: Mining Topic-level Influence in Heterogeneous Networks. In: CIKM 2010, Toronto, Canada (October 2010)Google Scholar
  21. 21.
    Alan Wang, G., Jiao, J., Abrahams, A.S., Fan, W., Zhang, Z.: ExpertRank: A topic-aware expert finding algorithm for online knowledge communities. Decision Support Systems 54, 1442–1451 (2013)CrossRefGoogle Scholar
  22. 22.
    Lim, S.-H., Kim, S.-W., Park, S., Lee, J.H.: Determining Content Power Users in a Blog Network: An Approach and Its Applications. IEEE Transactions on Systems, Man, and Cybernetics – part-A: System and Human 41(5), 853–862 (2011)CrossRefGoogle Scholar
  23. 23.
    Hao, F., Chen, M., Zhu, C., Guizani, M.: Discovering Influential Users in Micro-blog Marketing with Influence Maximization Mechanism. In: Globecom 2012 - Ad Hoc and Sensor Networking Symposium (2012)Google Scholar
  24. 24.
    Cai, K., Bao, S., Yang, Z., Tang, J., Ma, R., Zhang, L., Su, Z.: OOLAM: an Opinion Oriented Link Analysis Model for Influence Persona Discovery. In: WSDM 2011, Hong Kong, China, February 9-12 (2011)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Roshan Rabade
    • 1
  • Nishchol Mishra
    • 1
  • Sanjeev Sharma
    • 1
  1. 1.School of Information TechnologyRajiv Gandhi Proudyogiki VishwavidyalayaBhopalIndia

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