Efficient Community Detection in Heterogeneous Social Networks
Joint Authors
Pan, Zhisong
Hu, Guyu
Zhang, Yanyan
Li, Zhen
Li, Guopeng
Source
Mathematical Problems in Engineering
Issue
Vol. 2016, Issue 2016 (31 Dec. 2016), pp.1-15, 15 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2016-12-05
Country of Publication
Egypt
No. of Pages
15
Main Subjects
Abstract EN
Community detection is of great importance which enables us to understand the network structure and promotes many real-world applications such as recommendation systems.
The heterogeneous social networks, which contain multiple social relations and various user generated content, make the community detection problem more complicated.
Particularly, social relations and user generated content are regarded as link information and content information, respectively.
Since the two types of information indicate a common community structure from different perspectives, it is better to mine them jointly to improve the detection accuracy.
Some detection algorithms utilizing both link and content information have been developed.
However, most works take the private community structure of a single data source as the common one, and some methods take extra time transforming the content data into link data compared with mining directly.
In this paper, we propose a framework based on regularized joint nonnegative matrix factorization (RJNMF) to utilize link and content information jointly to enhance the community detection accuracy.
In the framework, we develop joint NMF to analyze link and content information simultaneously and introduce regularization to obtain the common community structure directly.
Experimental results on real-world datasets show the effectiveness of our method.
American Psychological Association (APA)
Li, Zhen& Pan, Zhisong& Zhang, Yanyan& Li, Guopeng& Hu, Guyu. 2016. Efficient Community Detection in Heterogeneous Social Networks. Mathematical Problems in Engineering،Vol. 2016, no. 2016, pp.1-15.
https://search.emarefa.net/detail/BIM-1112355
Modern Language Association (MLA)
Li, Zhen…[et al.]. Efficient Community Detection in Heterogeneous Social Networks. Mathematical Problems in Engineering No. 2016 (2016), pp.1-15.
https://search.emarefa.net/detail/BIM-1112355
American Medical Association (AMA)
Li, Zhen& Pan, Zhisong& Zhang, Yanyan& Li, Guopeng& Hu, Guyu. Efficient Community Detection in Heterogeneous Social Networks. Mathematical Problems in Engineering. 2016. Vol. 2016, no. 2016, pp.1-15.
https://search.emarefa.net/detail/BIM-1112355
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1112355