Multityped Community Discovery in Time-Evolving Heterogeneous Information Networks Based on Tensor Decomposition
Joint Authors
Wu, Jibing
Yu, Lianfei
Zhang, Qun
Shi, Peiteng
Liu, Lihua
Deng, Su
Huang, Hongbin
Source
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-16, 16 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-03-06
Country of Publication
Egypt
No. of Pages
16
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Wu, Jibing& Yu, Lianfei& Zhang, Qun& Shi, Peiteng& Liu, Lihua& Deng, Su…[et al.]. 2018. Multityped Community Discovery in Time-Evolving Heterogeneous Information Networks Based on Tensor Decomposition. Complexity،Vol. 2018, no. 2018, pp.1-16.
https://search.emarefa.net/detail/BIM-1136872
Modern Language Association (MLA)
Wu, Jibing…[et al.]. Multityped Community Discovery in Time-Evolving Heterogeneous Information Networks Based on Tensor Decomposition. Complexity No. 2018 (2018), pp.1-16.
https://search.emarefa.net/detail/BIM-1136872
American Medical Association (AMA)
Wu, Jibing& Yu, Lianfei& Zhang, Qun& Shi, Peiteng& Liu, Lihua& Deng, Su…[et al.]. Multityped Community Discovery in Time-Evolving Heterogeneous Information Networks Based on Tensor Decomposition. Complexity. 2018. Vol. 2018, no. 2018, pp.1-16.
https://search.emarefa.net/detail/BIM-1136872
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1136872