Approximate Solutions for the Influence Maximization Problem in a Social Network
We address the problem of maximizing the spread of information in a large-scale social network based on the Independent Cascade Model (ICM). When we solve the influence maximization problem, that is, the optimization problem of selecting the most influential nodes, we need to compute the expected number of nodes influenced by a given set of nodes. However, an exact calculation or a good estimate of this quantity needs a large amount of computation. Thus, very large computational quantities are needed to approximately solve the influence maximization problem based on a natural greedy algorithm. In this paper, we propose methods to efficiently obtain good approximate solutions for the influence maximization problem in the case where the propagation probabilities through links are small. Using real data on a large-scale blog network, we experimentally demonstrate the effectiveness of the proposed methods.
KeywordsSocial Network Greedy Algorithm Maximization Problem Submodular Function Approximation Guarantee
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