Department of Computer Science
Hierarchical community detection with applications to real-world network analysis
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. © 2012 Elsevier B.V.
Community detection, Graph mining, Link analysis, Social network analysis
Source Publication Title
Data and Knowledge Engineering
Link to Publisher's Edition
Yang, Bo, Jin Di, Jiming Liu, and Dayou Liu. "Hierarchical community detection with applications to real-world network analysis." Data and Knowledge Engineering 83 (2013): 20-38.