کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
378812 | 659221 | 2013 | 19 صفحه PDF | دانلود رایگان |
![عکس صفحه اول مقاله: Hierarchical community detection with applications to real-world network analysis Hierarchical community detection with applications to real-world network analysis](/preview/png/378812.png)
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.
Journal: Data & Knowledge Engineering - Volume 83, January 2013, Pages 20–38