Identifying Top-k Influential Nodes Based on Discrete Particle Swarm Optimization With Local Neighborhood Degree Centrality

The top-k influential individuals in a social network under specific topic play an important role reality. Identifying nodes of is still open and deeply-felt problem. In recent years, some researchers adopt the swarm intelligence algorithm to solve such problems obtain competitive results. There are two main models for intelligence, namely Ant Colony System (ACS) Particle Swarm Optimization (PSO). discretized basic Algorithm (DPSO) shows comparable performance identifying network. However, DPSO directly related choice its local search strategy. strategy based on greedy mechanism initial can easily lead global suboptimal solution due premature convergence algorithm. this paper, we degree centrality different neighbourhoods enhance ability. Through experiments, find that strategies have significant differences improvement algorithm's exploration capabilities, enhancement has saturation effect. Finally, best neighbourhood with improved ability, propose DPSO_NDC Experimental results six real-world networks show proposed outperforms other state-of-the-art algorithms influence nodes.