Spread of Information in a Social Network Using Influential Nodes
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.
Keywordsspread of information social network analysis maximum spanning tree k-center problem
Unable to display preview. Download preview PDF.
- 2.Domingos, P., Richardson, M.: Mining the network value of customers. In: KDD, pp. 57–66 (2001)Google Scholar
- 3.Richardson, M., Domingos, P.: Mining knowledge-sharing sites for viral marketing. In: KDD, pp. 61–70 (2002)Google Scholar
- 4.Leskovec, J., Adamic, L.A., Huberman, B.A.: The dynamics of viral marketing. TWEB 1(1) (2007)Google Scholar
- 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.Kempe, D., Kleinberg, J.M., Tardos, É.: Maximizing the spread of influence through a social network. In: KDD, pp. 137–146 (2003)Google Scholar
- 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
- 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.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.Dimitropoulos, X., Hyun, Y., Krioukov, D., Fomenkov, M., Riley, G., Huffaker, B.: As relationships: Inference and validation. Comput. Commun. Rev. (2007)Google Scholar
- 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