We study the problem of maximizing the expected spread of an innovation or behavior within a social network, in the presence of "word-of-mouth" referral. Our work builds on the observation that individuals' decisions to purchase a product or adopt an innovation are strongly influenced by recommendations from their friends and acquaintances. Understanding

of the corresponding interaction time. Furthermore, such interactions are quite fluid and evolving, as a result of which the topology of the underlying network may change rapidly, as new interactions form and others terminate. In such cases, it may be desirable to determine the influential nodes based on the dynamic

a social network ∗

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of individuals and their relationships, collaborations, or advice seeking relationships. The member or a node that has a large number of contacts with other nodes is referred as an information hub or influential node. Although members have numerous connections, only a fraction of those so called friends may

We consider the combinatorial optimization problem of finding the most influential nodes on a large-scale social network for two widely-used fundamental stochastic diffusion models. It was shown that a natural greedy strategy can give a good approximate solution to this optimization problem

social networks into “interest graphs”. We have proposed an improvised model for identifying influential nodes in multi-parameter social networks using these “interest graphs”. The experiments conducted on these interest graphs have shown better results than the method proposed in [8].

Abstract—Identifying the most influential nodes in social net-works is a key problem in social network analysis. However, without a strict definition of centrality the notion of what constitutes a central node in a network changes with application and the type of commodity flowing through a network

processes in a network modeled by a random graph with power law degree distribution. The first process V models the spread of a malicious piece of information, while the second one P starts with a certain delay w.r.t. V, and distributes a message which immunizes (uninfected) vertices. A node infected

of such social influence will help us better address the ques-tion of to what extent the ’word of mouth ’ effects will take hold on social networks. Most of the existing social in-fluence models to define the influence diffusion are solely based on topological connectivity of social network nodes. In this paper