离散粒子群社区检测算法

Community detection algorithm via discrete PSO

针对复杂网络中的社区检测问题,提出了一种基于节点影响力的离散粒子群社区检测方法。该方法以模块度密度作为目标函数,利用离散粒子群算法对其进行优化,在优化过程中提出了节点影响力的概念,其充分利用了网络中节点的相互关系检测网络中的社区结构。同时,在此基础上提出了基于节点影响力的粒子群初始化方法和粒子状态更新方法。利用人工网络数据集和真实网络数据集对所提算法进行测试,实验结果表明,所提算法具有较好的检测结果,能更好地对网络中社区进行划分。

Particle swarm optimization （PSO） is addressed into community detection problem, and an algo- rithm based on voting strategy is proposed. In contrast with other label propagation strategies, the main contri- bution is to take the impact of node into consideration, in which not only the number of nodes with the same la- bel in its neighbors, but also the degree of that node are considered. Special initialization and update approaches based on it are designed in order to make full use of it. Experiments on synthetic and real-life networks show the effectivity of proposed strategy. Experiments on real-life networks also demonstrate it is an efficacious way to solve community detection problem.