基于标签传播的拓扑势社区检测算法

基于拓扑势的社区检测通过节点的链接信息构造拓扑势域，在拓扑势域内进行社区划分.但实际划分过程存在大量孤立性社区.带节点属性信息的社区检测问题作为社区的重要组成，已成为社区检测的主要研究方向.本文提出了一种结合标签传播的拓扑势社区检测算法（TPCDLP）.首先，结合标签传播思想将属性信息转换为节点间的链接权值.其次，把链接权值加入到拓扑势中构造拓扑势域.再利用核心节点进行子群社区的划分.最后，利用子群社区间核心节点的距离进行社区划分.在3个含标签属性的数据集上，与6种算法对比，该算法在改进的模块度$Q_{ov}^E$、信息熵$Entropy$、社区重叠度$Overlap$和综合指标F上表现更优.在3个真实社区上应用了该算法，并与3种算法对比，实验结果显示该算法在标准化互信息指标$NMI$上表现良好，能够有效应用于实际问题.

Topological Potential Community Discovery Algorithm Based on Label Propagation

Community detection based on the topological potential constructs the topological potential field by the link information of nodes, in which the community can be partitioned. However, there are a large number of isolated communities in the actual division process. The problem of community discovery with node attribute information, as an important part of the community, has become the main research direction of community discovery. This paper proposes a topological potential community discovery algorithm combined with label propagation (TPCDLP). First, combining the thought of label propagation, the attribute information is converted into the link weights between nodes. Second, the link weights are added to the topological potential to construct the topological potential field. Then, the subgroup communities are partitioned by the core node. Finally, the communities are partitioned by using the distance of the core nodes between the subgroup communities. Compared with six algorithms on three datasets with label attributes, the TPCDLP performs better on the improved modular degree $Q_{ov}^E$, information entropy $Entropy$, community overlap degree $Overlap$ and comprehensive index $F$.