Identifying and evaluating community structure in complex networks
Pattern Recognition Letters
SBV-Cut: Vertex-cut based graph partitioning using structural balance vertices
Data & Knowledge Engineering
Modeling and detecting community hierarchies
SIMBAD'13 Proceedings of the Second international conference on Similarity-Based Pattern Recognition
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Community structure is ubiquitous in real-world networks and community detection is of fundamental importance in many applications. Although considerable efforts have been made to address the task, the objective of seeking a good trade-off between effectiveness and efficiency, especially in the case of large-scale networks, remains challenging. This paper explores the nature of community structure from a probabilistic perspective and introduces a novel community detection algorithm named as PMC, which stands for probabilistically mining communities, to meet the challenging objective. In PMC, community detection is modeled as a constrained quadratic optimization problem that can be efficiently solved by a random walk based heuristic. The performance of PMC has been rigorously validated through comparisons with six representative methods against both synthetic and real-world networks with different scales. Moreover, two applications of analyzing real-world networks by means of PMC have been demonstrated.