异质信息网络中基于元路径的社团发现算法研究

实际的网络化数据往往包含多种类型的对象和关系,采用异质信息网络可以更好地对其建模,因此异质信息网络分析逐渐成为数据挖掘的研究热点。虽然同质信息网络中的社团发现已经被深入研究,但是异质信息网络中的社团发现还很少被研究。该文研究异质信息网络中的社团发现问题,提出了一个新的社团发现算法框架HCD(heterogeneous community detection)。该框架由两部分组成: 基于单条元路径的社团发现算法HCD_sgl和融合多条元路径的社团发现算法HCD_all。HCD_sgl首先确定在给定元路径下所有节点的初始标签,再利用改进的标签传递算法进行最终的社团发现;HCD_all是在HCD_sgl的基础上将基于多条元路径的社团发现结果进行融合。通过在真实数据集和人工数据集上的实验验证了HCD算法的有效性。

Research on Community Detection Algorithm Based on Meta Path in Heterogeneous Information Network

The real networked data often contain different types of objects and relations,which can be better modeled with heterogeneous information network. Although the community detection in homogeneous information networks has been intensively studied,few works are done in heterogeneous information networks.In this paper,we study the community detection problem in heterogeneous information networks,and propose a novel method based on meta path called HCD (heterogeneous community detection). This method consists of two parts: a HCD_sgl algorithm based on single meta path,and a HCD_all algorithm combining multiple meta paths. The HCD_sgl decides the initial community label,then detecting the final community structures through the improved label propagation algorithm. HCD_all combined the results of multipie meta paths.Experiments on real dataset and artificial dataset demonstrate that the proposed method can detect community structures in heterogeneous information networks effectively.