应用离散量子粒子群的复杂网络社区检测

Community detection in complex networks based on discrete quantum particle swarm

针对模块度存在的解限制问题,分析了复杂网络社区检测中一种新的测度模块密度。采用二分策略,通过最大化模块密度,提出了基于离散量子粒子群优化进行复杂网络社区检测的算法。通过人工网络和现实网络的实验表明,算法具有较高的检测性能,并且在网络越来越模糊时,也能够检测出网络社区结构。

To overcome the resolution limits drawback of modularity function, a new measure of modularity density in complex network community detection is studied.With bi-partitioning strategy,by maximizing the module density,an algorithm is proposed based on discrete quantum particle swarm optimization for complex network community detection.Through the artificial network and real network experiments it is showed that this algorithm has high detection performance.And when the network becomes increasingly blurred,it can detect the network community structure well.