A Discrete Particle Swarm Optimization Algorithm Based on Neighbor Cognition to Solve the Problem of Social Influence Maximization

A Discrete Particle Swarm Optimization Algorithm Based on Neighbor Cognition to Solve the Problem of Social Influence Maximization

In view of the problem that the estimation method of node influence in social network is not comprehen-sive and the Particle Swarm Optimization (PSO) algorithm is easy to fall into the local optimal and the lo-cal search ability is insufficient. In this paper, we proposed a Neighbor Cognitive Discrete Particle Swarm Optimization (NCDPSO) algorithm. Aiming at the problem of influence in social networks, a new node influence measure method is proposed, the three-degree theory is introduced to comprehensively estimate the influence of nodes. In order to improve the global search ability of the PSO, the “neighbor cognition” factor is proposed to enhance the breadth of learning; and the following bee strategy is introduced to pro-pose particle density and survivability to control the number of elite clones, so as to solve the problem of insufficient local search ability of the algorithm. Finally, the validity of the proposed algorithm is verified by testing on real data sets and comparing with other algorithms.