Document Type

Journal Article

Department/Unit

Department of Computer Science

Title

Hierarchical community detection with applications to real-world network analysis

Language

English

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. © 2012 Elsevier B.V.

Keywords

Community detection, Graph mining, Link analysis, Social network analysis

Publication Date

2013

Source Publication Title

Data and Knowledge Engineering

Volume

83

Start Page

20

End Page

38

Publisher

Elsevier

DOI

10.1016/j.datak.2012.09.002

Link to Publisher's Edition

http://dx.doi.org/10.1016/j.datak.2012.09.002

ISSN (print)

0169023X

ISSN (electronic)

18726933

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