Maximum likelihood-based influence maximization in social networks

Influence Maximization (IM) is an important issue in network analyzing which widely occurs in social networks. The IM problem aims to detect the top-k influential seed nodes that can maximize the influence spread. Although a lot of studies have been performed, a novel algorithm with a better balance between time-consumption and guaranteed performance is still needed. In this work, we present a novel algorithm called MLIM for the IM problem, which adopts maximum likelihood-based scheme under the Independent Cascade(IC) model. We construct thumbnails of the social network and calculate the L-value for each vertex using the maximum likelihood criterion. A greedy algorithm is proposed to sequentially choose the seeds with the smallest L-value. Empirical results on real-world networks have proved that the proposed method can provide a wider influence spreading while obtaining lower time consumption.