Advertisement

Spread of Information in a Social Network Using Influential Nodes

  • Arpan Chaudhury
  • Partha Basuchowdhuri
  • Subhashis Majumder
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7302)

Abstract

Viral marketing works with a social network as its backbone, where social interactions help spreading a message from one person to another. In social networks, a node with a higher degree can reach larger number of nodes in a single hop, and hence can be considered to be more influential than a node with lesser degree. For viral marketing with limited resources, initially the seller can focus on marketing the product to a certain influential group of individuals, here mentioned as core. If k persons are targeted for initial marketing, then the objective is to find the initial set of k active nodes, which will facilitate the spread most efficiently. We did a degree based scaling in graphs for making the edge weights suitable for degree based spreading. Then we detect the core from the maximum spanning tree (MST) of the graph by finding the top k influential nodes and the paths in MST that joins them. The paths within the core depict the key interaction sequences that will trigger the spread within the network. Experimental results show that the set of k influential nodes found by our core finding method spreads information faster than the greedy k-center method for the same k value.

Keywords

spread of information social network analysis maximum spanning tree k-center problem 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Kiss, C., Bichler, M.: Identification of influencers - measuring influence in customer networks. Decision Support Systems 46, 233–253 (2008)CrossRefGoogle Scholar
  2. 2.
    Domingos, P., Richardson, M.: Mining the network value of customers. In: KDD, pp. 57–66 (2001)Google Scholar
  3. 3.
    Richardson, M., Domingos, P.: Mining knowledge-sharing sites for viral marketing. In: KDD, pp. 61–70 (2002)Google Scholar
  4. 4.
    Leskovec, J., Adamic, L.A., Huberman, B.A.: The dynamics of viral marketing. TWEB 1(1) (2007)Google Scholar
  5. 5.
    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, 211–223 (2001)CrossRefGoogle Scholar
  6. 6.
    Goldenberg, J., Libai, B., Muller, E.: Using complex systems analysis to advance marketing theory development: Modeling heterogeneity effects on new product growth through stochastic cellular automata. Academy of Marketing Science Review, 118 (2001)Google Scholar
  7. 7.
    Kempe, D., Kleinberg, J.M., Tardos, É.: Maximizing the spread of influence through a social network. In: KDD, pp. 137–146 (2003)Google Scholar
  8. 8.
    Kempe, D., Kleinberg, J.M., Tardos, É.: Influential Nodes in a Diffusion Model for Social Networks. In: Caires, L., Italiano, G.F., Monteiro, L., Palamidessi, C., Yung, M. (eds.) ICALP 2005. LNCS, vol. 3580, pp. 1127–1138. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  9. 9.
    Li, C.-T., Lin, S.-D., Shan, M.-K.: Finding influential mediators in social networks. In: WWW (Companion Volume), pp. 75–76 (2011)Google Scholar
  10. 10.
    Even-Dar, E., Shapira, A.: A Note on Maximizing the Spread of Influence in Social Networks. In: Deng, X., Graham, F.C. (eds.) WINE 2007. LNCS, vol. 4858, pp. 281–286. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  11. 11.
    Kimura, M., Saito, K.: Tractable Models for Information Diffusion in Social Networks. In: Fürnkranz, J., Scheffer, T., Spiliopoulou, M. (eds.) PKDD 2006. LNCS (LNAI), vol. 4213, pp. 259–271. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  12. 12.
    Kimura, M., Saito, K., Nakano, R., Motoda, H.: Extracting influential nodes on a social network for information diffusion. Data Min. Knowl. Discov. 20, 70–97 (2010)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Hochbaum, D.S., Shmoys, D.B.: A best possible heuristic for the k-center problem. Mathematics of Operations Research 10, 180–184 (1985)MathSciNetzbMATHCrossRefGoogle Scholar
  14. 14.
    Gonzalez, T.F.: Clustering to minimize the maximum intercluster distance. Theor. Comput. Sci. 38, 293–306 (1985)zbMATHCrossRefGoogle Scholar
  15. 15.
    Mihelic, J., Robic, B.: Solving the k-center problem efficiently with a dominating set algorithm. CIT 13, 225–234 (2005)CrossRefGoogle Scholar
  16. 16.
    Berger-Wolf, T.Y., Hart, W.E., Saia, J.: Discrete sensor placement problems in distribution networks. Mathematical and Computer Modelling 42, 1385–1396 (2005)MathSciNetzbMATHCrossRefGoogle Scholar
  17. 17.
    Lusseau, D., Newman, M.E.J.: Identifying the role that individual animals play in their social network. Proc. R. Soc. London B 271, S477 (2004)Google Scholar
  18. 18.
    Zachary, W.W.: An information flow model for conflict and fission in small groups. Journal of Anthropological Research 33, 452–473 (1977)Google Scholar
  19. 19.
    Dimitropoulos, X., Hyun, Y., Krioukov, D., Fomenkov, M., Riley, G., Huffaker, B.: As relationships: Inference and validation. Comput. Commun. Rev. (2007)Google Scholar
  20. 20.
    Bastian, M., Heymann, S., Jacomy, M.: Gephi: An open source software for exploring and manipulating networks. In: International AAAI Conference on Weblogs and Social Media (2009)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Arpan Chaudhury
    • 1
  • Partha Basuchowdhuri
    • 1
  • Subhashis Majumder
    • 1
  1. 1.Department of Computer Science and EngineeringHeritage Institute of TechnologyKolkataIndia

Personalised recommendations