Influence Maximization in Social Signed Multi-Networks

The Influence Maximization Problem addresses the challenge of selecting a set of spreaders with the aim of maximizing the spread influence on the network. In this work we propose a novel influence spreading model formulated for Social Signed Multi-Networks. We combine the assumption that only infected nodes express their opinion with the bounded confidence assumption. We solve the Influence Maximization Problem under this model by proposing a novel proxy-based Greedy method. We validate the effectiveness of our method through simulations on multi-networks constructed from two real online social signed networks, Epinions and Slashdot, and compare it against several baseline methods. The simulation results demonstrate that our proposed method consistently outperforms all other baselines in each case tested.