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

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

Wu, Jibing (National University of Defense Technology)
Yu, Lianfei (National University of Defense Technology; Army Academy of Border and Coastal Defense)
Zhang, Qun (National University of Defense Technology)
Shi, P. (TU Delft Computer Graphics and Visualisation)
Liu, Lihua (National University of Defense Technology)
Deng, Su (National University of Defense Technology)
Huang, Hongbin (National University of Defense Technology)

2018

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.

http://resolver.tudelft.nl/uuid:c79810f3-24a0-49cc-b8f0-e653cfb28be2

https://doi.org/10.1155/2018/9653404

1076-2787

Complexity, 2018, 1-16

Institutional Repository

journal article

© 2018 Jibing Wu, Lianfei Yu, Qun Zhang, P. Shi, Lihua Liu, Su Deng, Hongbin Huang