Effective Semisupervised Community Detection Using Negative Information
Joint Authors
Liu, Dong
Duan, Dequan
Sui, Shikai
Song, Guojie
Source
Mathematical Problems in Engineering
Issue
Vol. 2015, Issue 2015 (31 Dec. 2015), pp.1-8, 8 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2015-03-26
Country of Publication
Egypt
No. of Pages
8
Main Subjects
Abstract EN
The semisupervised community detection method, which can utilize prior information to guide the discovery process of community structure, has aroused considerable research interests in the past few years.
Most of the former works assume that the exact labels of some nodes are known in advance and presented in the forms of individual labels and pairwise constraints.
In this paper, we propose a novel type of prior information called negative information, which indicates whether a node does not belong to a specific community.
Then the semisupervised community detection algorithm is presented based on negative information to efficiently make use of this type of information to assist the process of community detection.
The proposed algorithm is evaluated on several artificial and real-world networks and shows high effectiveness in recovering communities.
American Psychological Association (APA)
Liu, Dong& Duan, Dequan& Sui, Shikai& Song, Guojie. 2015. Effective Semisupervised Community Detection Using Negative Information. Mathematical Problems in Engineering،Vol. 2015, no. 2015, pp.1-8.
https://search.emarefa.net/detail/BIM-1072927
Modern Language Association (MLA)
Liu, Dong…[et al.]. Effective Semisupervised Community Detection Using Negative Information. Mathematical Problems in Engineering No. 2015 (2015), pp.1-8.
https://search.emarefa.net/detail/BIM-1072927
American Medical Association (AMA)
Liu, Dong& Duan, Dequan& Sui, Shikai& Song, Guojie. Effective Semisupervised Community Detection Using Negative Information. Mathematical Problems in Engineering. 2015. Vol. 2015, no. 2015, pp.1-8.
https://search.emarefa.net/detail/BIM-1072927
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1072927