Years and Authors of Summarized Original Work
2011; Lu, Zhang, Wu, Fu, Du
2012; Lu, Zhang, Wu, Kim, Fu
One of the fundamental problems in social network is influence maximization. Informally, if we can convince a small number of individuals in a social network to adopt a new product or innovation, and the target is to trigger a maximum further adoptions, then which set of individuals should we convince? Consider a social network as a graph G(V, E) consisting of individuals (node set V ) and relationships (edge set E); essentially influence maximization comes down to the problem of finding important nodes or structures in graphs.
In order to address the influence maximization problem, first it is needed to understand the influence diffusion process in social networks. In other words, how does the influence propagate over time through a social network? Assume time is partitioned into discrete time slots, and then influence diffusion can be modeled...
KeywordsApproximation algorithm Influence maximization NP-hard Social network
- 1.Chen W, Yuan Y, Zhang L (2010) Scalable influence maximization in social networks under the linear threshold model. In: The 2010 international conference on data mining. Sydney, AustraliaGoogle Scholar
- 2.Chen W, Wang C, Wang Y (2009) Scalable influence maximization for prevalent viral marketing in large-scale social networks. In: The 2010 international conference on knowledge discovery and data mining. Washington DC, USAGoogle Scholar
- 4.Domingos P, Richardson M (2001) Mining the network value of customers. In: The 2001 international conference on knowledge discovery and data mining. San Francisco, CA, USAGoogle Scholar
- 5.Goldenberg J, Libai B, Muller E (2001) Using complex systems analysis to advance marketing theory development. Acad Mark Sci Rev 9(3):1–18Google Scholar
- 6.Kempe D, Kleinberg J, Tardos É (2003) Maximizing the spread of influence through a social network. In: The 2003 international conference on knowledge discovery and data mining. Washington DC, USAGoogle Scholar
- 7.Kempe D, Kleinberg J, Tardos É (2005) Influential nodes in a diffusion model for social networks. In: The 2005 international colloquium on automata, languages and programming. Lisbon, PortugalGoogle Scholar
- 8.Richardson M, Domingos P (2002) Mining knowledge-sharing sites for viral marketing. In: The 2002 international conference on knowledge discovery and data mining. Edmonton, AB, CanadaGoogle Scholar
- 10.Schelling T (1978) Micromotives and macrobehavior. Norton, New YorkGoogle Scholar
- 13.Lu Z, Zhang W, Wu W, Fu B, Du D (2011) Approximation and inapproximation for the influence maximization problem in social networks under deterministic linear threshold model. In: The 2011 international conference on distributed computing systems workshops. Minneapolis, USAGoogle Scholar