Extremal optimization-based semi-supervised algorithm with conflict pairwise constraints for community detection

Lei Li, Mei Du, Guanfeng Liu, Xuegang Hu, Gongqing Wu

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contribution

8 Citations (Scopus)

Abstract

The research on community structure is a key to analyze the network functionality and topology, and thus it is significant to detect and analysis the community structure. During the abstract process from an actual system to a network, especially for a large-scale network, it is inevitable to have mistaken connections between nodes or have connection missing. In addition, in real applications, from time to time we can obtain prior information in the form of pairwise constraints between nodes besides topology information, although they may be inaccurate or conflicted. These noises in the network-related information will dramatically reduce the accuracy of community detection. Hence, in this paper, we introduce a dissimilarity index to determine the trustworthiness of pairwise constraints and settle the conflict of pairwise constraints. Then, focusing on the community detection with false connections or conflicted connections, we propose a pairwise constrained structure-enhanced extremal optimization-based semi-supervised algorithm (PCSEO-SS algorithm). Compared with existing semi-supervised community detection approaches, the experimental results executed on real networks and synthetic networks, show that PCSEO-SS can solve the problem of false connections or conflicted connections to some extent and detect the community structure more precisely.

Original languageEnglish
Title of host publicationProceedings of the 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
Subtitle of host publicationASONAM 2014
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages180-187
Number of pages8
ISBN (Electronic)9781479958771
ISBN (Print)9781479958764
DOIs
Publication statusPublished - 10 Oct 2014
Externally publishedYes
Event2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2014 - Beijing, China
Duration: 17 Aug 201420 Aug 2014

Other

Other2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2014
CountryChina
CityBeijing
Period17/08/1420/08/14

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  • Cite this

    Li, L., Du, M., Liu, G., Hu, X., & Wu, G. (2014). Extremal optimization-based semi-supervised algorithm with conflict pairwise constraints for community detection. In Proceedings of the 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining: ASONAM 2014 (pp. 180-187). [6921580] Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/ASONAM.2014.6921580