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Title:      A TWO-PHASE STRATEGY FOR DETECTING COMMUNITIES
Author(s):      Kamal Hassan Taha
ISBN:      978-989-8533-54-8
Editors:      Piet Kommers and Guo Chao Peng
Year:      2016
Edition:      Single
Keywords:      Networks, community structure, community detection
Type:      Full Paper
First Page:      113
Last Page:      120
Language:      English
Cover:      cover          
Full Contents:      click to dowload Download
Paper Abstract:      One of the key objectives for representing real-world problems using networks is for detecting community structures. This is because detecting community structure is crucial for identifying the link between structure and function in complex networks, which is the key for solving many practical applications in various disciplines. The detection of “good” communities has proven to be a challenging task. This is due, mainly, to the fact that most current methods detect communities in independents. As a result, most of them do not work well on highly sparse networks. We propose in this paper a system called TPSDC that detects disjoint communities and works well on highly sparse networks. It does so by adopting the following procedure: (1) assigning a score to each vertex to reflect its relative importance to the whole network, (2) assigning a score to each link connecting two neighboring vertices to represent the degree of association between them, (3) employing a two-phase strategy for detecting disjoint communities, and (4) enhancing the density of community using a post-processing technique. We evaluated the quality of TPSDC by comparing it experimentally with nine methods. Results showed marked improvement.
   

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