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

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