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, B., Di, J., Liu, J., & Liu, D. (2013). Hierarchical community detection with applications to real-world network analysis. Data and Knowledge Engineering, 83, 20-38. https://doi.org/10.1016/j.datak.2012.09.002