Finding Communities by Decomposing and Embedding Heterogeneous Information Network  

作　　者：Yue Kou De-Rong Shen Dong Li Tie-Zheng Nie Ge Yu 

机构地区：[1]School of Computer Science and Engineering,Northeastern University,Shenyang 110004,China

出　　处：《Journal of Computer Science & Technology》2020年第2期320-337,共18页计算机科学技术学报（英文版）

基　　金：The work was supported by the National Key Research and Development Program of China under Grant No.2018YFB1003404;the National Natural Science Foundation of China under Grant Nos.61672142,U1435216 and 61602103.

摘　　要：Community discovery is an important task in social network analysis.However,most existing methods for community discovery rely on the topological structure alone.These methods ignore the rich information available in the content data.In order to solve this issue,in this paper,we present a community discovery method based on heterogeneous information network decomposition and embedding.Unlike traditional methods,our method takes into account topology,node content and edge content,which can supply abundant evidence for community discovery.First,an embedding-based similarity evaluation method is proposed,which decomposes the heterogeneous information network into several subnetworks,and extracts their potential deep representation to evaluate the similarities between nodes.Second,a bottom-up community discovery algorithm is proposed.Via leader nodes selection,initial community generation,and community expansion,communities can be found more efficiently.Third,some incremental maintenance strategies for the changes of networks are proposed.We conduct experimental studies based on three real-world social networks.Experiments demonstrate the effectiveness and the efficiency of our proposed method.Compared with the traditional methods,our method improves normalized mutual information(NMI)and the modularity by an average of 12%and 37%respectively.

关 键 词：COMMUNITY DISCOVERY HETEROGENEOUS information network decomposition EMBEDDING INCREMENTAL maintenance 

分 类 号：TP39[自动化与计算机技术—计算机应用技术]