A Tensor CP Decomposition Method for Clustering Heterogeneous Information Networks via Stochastic Gradient Descent Algorithms
Joint Authors
Wu, Jibing
Liu, Lihua
Deng, Su
Huang, Hongbin
Wang, Zhifei
Wu, Yahui
Source
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-13, 13 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-04-30
Country of Publication
Egypt
No. of Pages
13
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Wu, Jibing& Wang, Zhifei& Wu, Yahui& Liu, Lihua& Deng, Su& Huang, Hongbin. 2017. A Tensor CP Decomposition Method for Clustering Heterogeneous Information Networks via Stochastic Gradient Descent Algorithms. Scientific Programming،Vol. 2017, no. 2017, pp.1-13.
https://search.emarefa.net/detail/BIM-1203333
Modern Language Association (MLA)
Wu, Jibing…[et al.]. A Tensor CP Decomposition Method for Clustering Heterogeneous Information Networks via Stochastic Gradient Descent Algorithms. Scientific Programming No. 2017 (2017), pp.1-13.
https://search.emarefa.net/detail/BIM-1203333
American Medical Association (AMA)
Wu, Jibing& Wang, Zhifei& Wu, Yahui& Liu, Lihua& Deng, Su& Huang, Hongbin. A Tensor CP Decomposition Method for Clustering Heterogeneous Information Networks via Stochastic Gradient Descent Algorithms. Scientific Programming. 2017. Vol. 2017, no. 2017, pp.1-13.
https://search.emarefa.net/detail/BIM-1203333
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1203333