On Fast and Effective Influence Spread Maximization in Social Networks

The ability of social networks to disseminate information across individuals and groups is based on the social influence that people have on each other.

In this context, the so-called influence maximization problem consists in identifying the most influential nodes of a given social network. 

This problem has practical relevance in a wide range of applications and despite the underlying computational complexity, several solution techniques have been presented in the related literature. Nonetheless, bringing together technical feasibility and satisfactory accuracy is still a challenging open research issue.

In this work, we address the influence maximization problem with two complementary contributions. First, we analyze two well-known influence diffusion models, namely the independent cascade and the linear threshold models, and provide a new methodology for addressing the problem of computing the influence spread in a given network. Subsequently, we propose a novel approach to select an initial set of nodes that optimizes the influence spread in large-scale scenarios.

We apply these techniques to several large-scale experiments in real-world scenarios achieving results comparable with the best-performing state-of-the-art algorithms in a shorter computational time.

This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible.