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Scientific Programming
Volume 2017, Article ID 2803091, 13 pages
https://doi.org/10.1155/2017/2803091
Research Article

A Tensor CP Decomposition Method for Clustering Heterogeneous Information Networks via Stochastic Gradient Descent Algorithms

Science and Technology on Information System Engineering Laboratory, National University of Defense Technology, Changsha, China

Correspondence should be addressed to Hongbin Huang; nc.ude.tdun@gnauhbh

Received 1 December 2016; Revised 14 February 2017; Accepted 16 February 2017; Published 30 April 2017

Academic Editor: Fabrizio Riguzzi

Copyright © 2017 Jibing Wu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Clustering analysis is a basic and essential method for mining heterogeneous information networks, which consist of multiple types of objects and rich semantic relations among different object types. Heterogeneous information networks are ubiquitous in the real-world applications, such as bibliographic networks and social media networks. Unfortunately, most existing approaches, such as spectral clustering, are designed to analyze homogeneous information networks, which are composed of only one type of objects and links. Some recent studies focused on heterogeneous information networks and yielded some research fruits, such as RankClus and NetClus. However, they often assumed that the heterogeneous information networks usually follow some simple schemas, such as bityped network schema or star network schema. To overcome the above limitations, we model the heterogeneous information network as a tensor without the restriction of network schema. Then, a tensor CP decomposition method is adapted to formulate the clustering problem in heterogeneous information networks. Further, we develop two stochastic gradient descent algorithms, namely, SGDClus and SOSClus, which lead to effective clustering multityped objects simultaneously. The experimental results on both synthetic datasets and real-world dataset have demonstrated that our proposed clustering framework can model heterogeneous information networks efficiently and outperform state-of-the-art clustering methods.