Department of Computer Science
Community mining from signed social networks
Many complex systems in the real world can be modeled as signed social networks that contain both positive and negative relations. Algorithms for mining social networks have been developed in the past; however, most of them were designed primarily for networks containing only positive relations and, thus, are not suitable for signed networks. In this work, we propose a new algorithm, called FEC, to mine signed social networks where both positive within-group relations and negative between-group relations are dense. FEC considers both the sign and the density of relations as the clustering attributes, making it effective for not only signed networks but also conventional social networks including only positive relations. Also, FEC adopts an agent-based heuristic that makes the algorithm efficient (in linear time with respect to the size of a network) and capable of giving nearly optimal solutions. FEC depends on only one parameter whose value can easily be set and requires no prior knowledge on hidden community structures. The effectiveness and efficacy of FEC have been demonstrated through a set of rigorous experiments involving both benchmark and randomly generated signed networks.
community mining, agent-based approach, random walk, signed social networks, Social network services, Clustering algorithms, Polynomials, Algorithm design and analysis, Joining processes, Humans, Robustness
Source Publication Title
IEEE Transactions on Knowledge and Data Engineering
Institute of Electrical and Electronics Engineers (IEEE)
The work reported in this paper was carried out in the Center for e-Transformation Research, Hong Kong Baptist University, and supported by the Hong Kong RGC Central Allocation Grant HKBU 2/03/C. B. Yang was also supported by the National Natural Science Foundation of China Grant 60503016.
Link to Publisher's Edition
Yang, Bo, William K. Cheung, and Jiming Liu. "Community mining from signed social networks." IEEE Transactions on Knowledge and Data Engineering 19.10 (2007): 1333-1348.