基于语义路径的异质网络社区发现方法

社区发现是社会网络研究的热点问题，综合利用社会网络中不同对象间的异质信息，可以更加有效地挖掘网络中的社区结构。针对传统的社区发现方法无法有效地利用异质信息的问题，本文提出了一种基于语义路径的异质网络社区发现方法，该方法首先定义网络中的语义路径，通过语义路径来衡量不同类型对象间的异质信息相似度，然后以此构造可靠性矩阵，作为半监督非负矩阵分解的正则化约束项，进而实现异质网络的社区划分。在真实数据集上的实验结果表明，所提出的方法能够更准确地发现异质网络中的社区结构。

Co mmunity Detection in Heterogeneous Network with Se mantic Paths

Community detection is an important and crucial issue in social networks.Using different objects’informa-tion can help detect the community structure.However,many existing community detection methods are hardly applied in heterogeneous networks.To address the above problem,we propose a semantic-path based community detection method.This method first calculates the similarity matrix based on semantic paths,obtaining the reliability matrix to build a graph regulari-zation term.Then the nonnegative matrix factorization is employed to achieve the community detection in heterogeneous net-works.Simulation on real web data demonstrates that our proposed algorithm can detect the community structure in heteroge-neous networks.