基于链接相似性聚类的重叠社区识别

社区结构是社会网络最普遍和重要的拓扑属性之一,提出一种基于链接相似性聚类的重叠社区识别算法.该算法首先根据相邻链接的度分布状态,提出链接间的相似性度量方法;其次以链接相似性矩阵为输入,以链接社区的最优划分为目标,建立链接局部相似性聚类算法,实现了重叠社区的有效识别;然后对链接社区进行优化,解决了可能出现的过度重叠及孤立社区问题;最后在真实网络及人工合成网络上的实验验证了算法的高效性.

Overlapping Community Detection Based on Link Similarity Clustering

Community structure is one of the most common and important social network topological properties.This paper proposes a link community detection algorithm based on hierarchical clustering.Firstly,the algorithm sets up similarity measure according to the degree distribution of links nearby;then sets up local link similarity clustering algorithm which takes the similarity matrix as input with the purpose of detecting the best link community;further more realizes link community detection effectively.And then,optimize the link community to solve the problem of excessive overlapping and isolated community.Experiment results based on real world and computer generated networks show that the algorithm is highly efficient.