Influence maximization in social networks based on discrete particle swarm optimization

Abstract

Influence maximization in social networks aims to find a small group of individuals, which have maximal influence cascades. In this study, an optimization model based on a local influence criterion is established for the influence maximization problem. The local influence criterion can provide a reliable estimation for the influence propagations in independent and weighted cascade models. A discrete particle swarm optimization algorithm is then proposed to optimize the local influence criterion. The representations and update rules for the particles are redefined in the proposed algorithm. Moreover, a degree based heuristic initialization strategy and a network-specific local search strategy are introduced to speed up the convergence. Experimental results on four real-world social networks demonstrate the effectiveness and efficiency of the proposed algorithm for influence maximization.