Seeds selection for spreading in a weighted cascade model

Abstract

In this paper, we investigate the problem of selecting seeds for spreading in complex networks. We first propose a weighted cascade model in which a node in the network is activated with a probability related to its neighbor’s degree which represents the neighbor’s persuasiveness. Furthermore, we provide several heuristic algorithms with little computational burden for selecting seeds to start the spreading. In particular, we propose a heuristic algorithm based on the expected benefit of selecting a seed. By simulations on two real networks, we find that the proposed algorithm based on expected benefit is better than the classic network centralities, including degree and PageRank.