Influence Maximization with an Unknown Network by Exploiting Community Structure

In many real world applications of influence maximization, practitioners intervene in a population whose social structure is initially unknown. We formalize this problem by introducing exploratory influence maximization, in which an algorithm queries individual network nodes to learn their links. The goal is to locate a seed set nearly as influential as the global optimum using very few queries. We show that this problem is intractable for general graphs. However, real world networks typically have community structure, in which nodes are arranged in densely connected subgroups. We present the ARISEN algorithm, which leverages community structure to find an influential seed set by querying only a fraction of the network. Experiments on real world networks of homeless youth, village populations in India, and others validate ARISEN’s performance.

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