基于随机游走的进化计算社区发现

Community detection through random-walk-based evolutionary computation

针对目前进化计算社区发现方法精度不高与社区边界识别较低的问题,提出一种基于随机游走的进化计算社区发现算法.首先,设计一种基于拓扑及属性信息随机游走的社区初始化策略,以准确识别社区边界,提高社区发现的精度.其次,设计综合考虑拓扑和属性的节点嵌入向量更新策略,使节点的属性信息能够在进化过程中被有效利用,以提高社区划分的质量.通过在真实和人工数据集上实验,验证了所提出的新算法比现有方法有更好的社区划分.

In this paper,we propose a random walk based evolutionary computing community discovery algorithm to solve the problem of low accuracy of current evolutionary computing community discovery methods and low community boundary recognition.First,a community initialization strategy based on the random walk on topology and attributes is designed to identify community boundaries precisely and improve the accuracy of community discovery.Second,a node embedding vector updating strategy considering topology and attributes is designed to allow the attribute information of nodes to be used in the evolution process effectively to improve the quality of community division.Experiments on real-world and artificial datasets verify that the proposed algorithm can achieve better community partitions than existing methods.