Multityped Community Discovery in Time-Evolving Heterogeneous Information Networks Based on Tensor Decomposition

Jibing Wu, Lianfei Yu, Qun Zhang, Peiteng Shi, Lihua Liu, Su Deng, Hongbin Huang

Research output: Contribution to journalArticleScientificpeer-review

4 Citations (Scopus)
113 Downloads (Pure)


The heterogeneous information networks are omnipresent in real-world applications, which consist of multiple types of objects with various rich semantic meaningful links among them. Community discovery is an effective method to extract the hidden structures in networks. Usually, heterogeneous information networks are time-evolving, whose objects and links are dynamic and varying gradually. In such time-evolving heterogeneous information networks, community discovery is a challenging topic and quite more difficult than that in traditional static homogeneous information networks. In contrast to communities in traditional approaches, which only contain one type of objects and links, communities in heterogeneous information networks contain multiple types of dynamic objects and links. Recently, some studies focus on dynamic heterogeneous information networks and achieve some satisfactory results. However, they assume that heterogeneous information networks usually follow some simple schemas, such as bityped network and star network schema. In this paper, we propose a multityped community discovery method for time-evolving heterogeneous information networks with general network schemas. A tensor decomposition framework, which integrates tensor CP factorization with a temporal evolution regularization term, is designed to model the multityped communities and address their evolution. Experimental results on both synthetic and real-world datasets demonstrate the efficiency of our framework.
Original languageEnglish
Article number9653404
Pages (from-to)1-16
Number of pages16
Publication statusPublished - 2018


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