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.
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References
Kiss, C., Bichler, M.: Identification of influencers - measuring influence in customer networks. Decision Support Systems 46, 233–253 (2008)
Domingos, P., Richardson, M.: Mining the network value of customers. In: KDD, pp. 57–66 (2001)
Richardson, M., Domingos, P.: Mining knowledge-sharing sites for viral marketing. In: KDD, pp. 61–70 (2002)
Leskovec, J., Adamic, L.A., Huberman, B.A.: The dynamics of viral marketing. TWEBÂ 1(1) (2007)
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)
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)
Kempe, D., Kleinberg, J.M., Tardos, É.: Maximizing the spread of influence through a social network. In: KDD, pp. 137–146 (2003)
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)
Li, C.-T., Lin, S.-D., Shan, M.-K.: Finding influential mediators in social networks. In: WWW (Companion Volume), pp. 75–76 (2011)
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)
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)
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)
Hochbaum, D.S., Shmoys, D.B.: A best possible heuristic for the k-center problem. Mathematics of Operations Research 10, 180–184 (1985)
Gonzalez, T.F.: Clustering to minimize the maximum intercluster distance. Theor. Comput. Sci. 38, 293–306 (1985)
Mihelic, J., Robic, B.: Solving the k-center problem efficiently with a dominating set algorithm. CIT 13, 225–234 (2005)
Berger-Wolf, T.Y., Hart, W.E., Saia, J.: Discrete sensor placement problems in distribution networks. Mathematical and Computer Modelling 42, 1385–1396 (2005)
Lusseau, D., Newman, M.E.J.: Identifying the role that individual animals play in their social network. Proc. R. Soc. London BÂ 271, S477 (2004)
Zachary, W.W.: An information flow model for conflict and fission in small groups. Journal of Anthropological Research 33, 452–473 (1977)
Dimitropoulos, X., Hyun, Y., Krioukov, D., Fomenkov, M., Riley, G., Huffaker, B.: As relationships: Inference and validation. Comput. Commun. Rev. (2007)
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)
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Chaudhury, A., Basuchowdhuri, P., Majumder, S. (2012). Spread of Information in a Social Network Using Influential Nodes. In: Tan, PN., Chawla, S., Ho, C.K., Bailey, J. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2012. Lecture Notes in Computer Science(), vol 7302. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30220-6_11
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DOI: https://doi.org/10.1007/978-3-642-30220-6_11
Publisher Name: Springer, Berlin, Heidelberg
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