Abstract
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.
Original language | English |
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Pages (from-to) | 20-38 |
Number of pages | 19 |
Journal | Data and Knowledge Engineering |
Volume | 83 |
DOIs | |
Publication status | Published - Jan 2013 |
User-Defined Keywords
- Community detection
- Graph mining
- Link analysis
- Social network analysis