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Issue No. 02 - Mar.-Apr. (2014 vol. 18)
ISSN: 1089-7801
pp: 24-31
Fei Hao , Huazhong University of Science and Technology
Stephen S. Yau , Arizona State University
Geyong Min , University of Bradford
Laurence T. Yang , Huazhong University of Science and Technology
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.
Social network services, Privacy, Trust management, Network security, Online services, Authentication, Handwriting recognition

F. Hao, S. S. Yau, G. Min and L. T. Yang, "Detecting k-Balanced Trusted Cliques in Signed Social Networks," in IEEE Internet Computing, vol. 18, no. 2, pp. 24-31, 2014.
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