The Community for Technology Leaders
2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) (2014)
China
Aug. 17, 2014 to Aug. 20, 2014
ISBN: 978-1-4799-5877-1
pp: 180-187
Lei Li , School of Computer Science and Information Engineering, Hefei University of Technology, Anhui, 230009, China
Mei Du , School of Computer Science and Information Engineering, Hefei University of Technology, Anhui, 230009, China
Guanfeng Liu , Soochow Advanced Data Analytics Lab, Soochow University, Suzhou, Jiangsu, 215006, China
Xuegang Hu , School of Computer Science and Information Engineering, Hefei University of Technology, Anhui, 230009, China
Gongqing Wu , School of Computer Science and Information Engineering, Hefei University of Technology, Anhui, 230009, China
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.
INDEX TERMS
Communities, Optimization, Detection algorithms, Indexes, Accuracy, Algorithm design and analysis, Noise
CITATION

L. Li, M. Du, G. Liu, X. Hu and G. Wu, "Extremal optimization-based semi-supervised algorithm with conflict pairwise constraints for community detection," 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), China, 2014, pp. 180-187.
doi:10.1109/ASONAM.2014.6921580
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