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....Show MoreMetadata
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.
Published in: 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014)
Date of Conference: 17-20 August 2014
Date Added to IEEE Xplore: 16 October 2014
Electronic ISBN:978-1-4799-5877-1