IDPSO for Influence Maximization under Independent Cascade Model

Influence maximization aims to find the most influential set of nodes from the network, through which specific information can achieve the widest dissemination in the network. The traditional influence maximization algorithm based on a greedy framework is challenging to apply to large networks due to the excessive computational overhead, and the heuristic algorithm is less than satisfactory in performance. In order to solve the imbalance between the running time and implementation of the influence maximization algorithm, we propose a local influence evaluation function to calculate the influence spread of the seed set in the network, reducing the time consumption. Then, we design an influence maximization algorithm based on improved discrete particle swarm optimization for the independent cascade model. Experiments on real social network datasets demonstrate that the proposed algorithm has superior running time and performance.