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Detecting k-Balanced Trusted Cliques in Signed Social Networks
Mar.-Apr. 2014 (vol. 18 no. 2)
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.
Index Terms:
Social network services,Privacy,Trust management,Network security,Online services,Authentication,Handwriting recognition,signed social networks,FCA,equiconcept,trusted cliques
Citation:
Fei Hao, Stephen S. Yau, Geyong Min, Laurence T. Yang, "Detecting k-Balanced Trusted Cliques in Signed Social Networks," IEEE Internet Computing, vol. 18, no. 2, pp. 24-31, Mar.-Apr. 2014, doi:10.1109/MIC.2014.25
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