计算机工程与应用2019，Vol.55Issue(1)：56-63,8.DOI:10.3778/j.issn.1002-8331.1709-0335
融合拓扑势的社交网络层次化社区发现算法
Hierarchical Community Discovery Algorithm for Social Network on Topology Potential
摘要
Abstract
Social networks often demonstrate hierarchical community structures. Most traditional agglomerative hierarchical community detection methods are often inefficient, and the generated dendrograms are complex. Motivated by solving these problems, the paper proposes a novel hierarchical community detection method on topology potential. The method employs the natural peak-valley structure of topology potential field to reveal the hierarchical relationship among commu-nities in social network. The proposed method firstly identifies the local maximal potential nodes and detects the initial local community structure of the network on the basis of these nodes. After that, the initial local communities are iteratively merged according to the distance among the maximal potential nodes until all the communities are merged into one community. Experimental results on both synthetic and real-world networks show that the proposed method can discover the hierarchical community structure efficiency, and the generated dendrogram is simple and intuitive.关键词
社交网络/层次社区/拓扑势/峰谷结构Key words
social network/hierarchical community/topology potential/peak-valley structure分类
信息技术与安全科学引用本文复制引用
候梦男,王志晓,何婧,芮晓彬,高菊远..融合拓扑势的社交网络层次化社区发现算法[J].计算机工程与应用,2019,55(1):56-63,8.基金项目
国家自然科学基金(No.61402482) （No.61402482）
中国博士后基金(No.2015T80555) （No.2015T80555）
江苏省博士后基金(No.1501012A). （No.1501012A）
