Effective identification of multiple influential spreaders by DegreePunishment

With the rapid development of social networks, how to effectively identify a small group of nodes to maximize their spreading influence becomes a crucial topic. Traditional centrality-based methods are often very simple but not so effective compared to other complex methods. In this paper, we propose a heuristic method to select spreaders sequentially by carrying out a punishing strategy to the neighbors of those already selected spreaders. We use the Susceptible-Infected-Recovered (SIR) model to evaluate the performance by considering the number of infected nodes in the end. Experiments on four real networks show that our method outperforms traditional centrality-based methods and several heuristic ones.