Detecting k-Balanced Trusted Cliques in Signed Social Networks
Hao, Fei; Yau, SS; Min, Geyong; et al.Yang, LT
Date: 24 March 2014
Journal
IEEE Internet Computing
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Publisher DOI
Abstract
k-Clique detection enables computer scientists and sociologists to analyze social networks' latent structure and thus understand their structural and functional properties. However, the existing k-clique-detection approaches are not applicable to signed social networks directly because of positive and negative links. The authors' ...
k-Clique detection enables computer scientists and sociologists to analyze social networks' latent structure and thus understand their structural and functional properties. However, the existing k-clique-detection approaches are not applicable to signed social networks directly because of positive and negative links. The authors' approach to detecting k-balanced trusted cliques in such networks bases the detection algorithm on formal context analysis. It constructs formal contexts using the modified adjacency matrix after converting a signed social network into an unweighted one. Experimental results demonstrate that their algorithm can efficiently identify the trusted cliques.
Computer Science
Faculty of Environment, Science and Economy
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