Influence Maximization Problem for Unknown Social Networks

We propose a novel problem called influence maximization for unknown graphs, and propose a heuristic algorithm for the problem. Influence maximization is the problem of detecting a set of influential nodes in a social network, which represents social relationships among individuals. Influence maximization has been actively studied, and several algorithms have been proposed in the literature. The existing algorithms use the entire topological structure of a social network. In practice, however, complete knowledge of the topological structure of a social network is typically difficult to obtain. We therefore tackle an influence maximization problem for unknown graphs. As a solution for this problem, we propose a heuristic algorithm, which we call IMUG (Influence Maximization for Unknown Graphs). Through extensive simulations, we show that the proposed algorithm achieves 60-90% of the influence spread of the algorithms using the entire social network topology, even when only 1-10% of the social network topology is known. These results indicate that we can achieve a reasonable influence spread even when knowledge of the social network topology is severely limited.