Leveraging the Community Structure of a Social Network for Maximizing the Spread of Influence.

We consider the problem of Influence Maximization (IM), the task of selecting k seed nodes in a social network such that the expected number of nodes influenced is maximized. We propose a community-aware divide-and-conquer framework that involves (i) learning the inherent community structure of the social network, (ii) generating candidate solutions by solving the influence maximization problem for each community, and (iii) selecting the final set of seed nodes using a novel progressive budgeting scheme. Our experiments on a real-world social network show that the proposed framework outperforms the standard methods in terms of run-time and the heuristic methods in terms of influence.