No / 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.
| Identifer | oai:union.ndltd.org:BRADFORD/oai:bradscholars.brad.ac.uk:10454/10645 |
| Date | January 2014 |
| Creators | Hao, F., Yau, S.S., Min, Geyong, Yang, L.T. |
| Source Sets | Bradford Scholars |
| Detected Language | English |
| Type | Article, No full-text available in the repository |
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