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Detecting k-Balanced Trusted Cliques in Signed Social Networks

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4 Author(s)
Hao, Fei ; Huazhong University of Science and Technology ; Yau, Stephen S. ; Min, Geyong ; Yang, Laurence T.

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.

Published in:

Internet Computing, IEEE  (Volume:18 ,  Issue: 2 )