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Discovering Link Communities In Complex Networks By An Integer Programming Model And A Genetic Algorithm. by Li%2c Zhenping
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1Discovering Link Communities In Complex Networks By An Integer Programming Model And A Genetic Algorithm.
By Li, Zhenping, Zhang, Xiang-Sun, Wang, Rui-Sheng, Liu, Hongwei and Zhang, Shihua
This article is from PLoS ONE , volume 8 . Abstract Identification of communities in complex networks is an important topic and issue in many fields such as sociology, biology, and computer science. Communities are often defined as groups of related nodes or links that correspond to functional subunits in the corresponding complex systems. While most conventional approaches have focused on discovering communities of nodes, some recent studies start partitioning links to find overlapping communities straightforwardly. In this paper, we propose a new quantity function for link community identification in complex networks. Based on this quantity function we formulate the link community partition problem into an integer programming model which allows us to partition a complex network into overlapping communities. We further propose a genetic algorithm for link community detection which can partition a network into overlapping communities without knowing the number of communities. We test our model and algorithm on both artificial networks and real-world networks. The results demonstrate that the model and algorithm are efficient in detecting overlapping community structure in complex networks.
“Discovering Link Communities In Complex Networks By An Integer Programming Model And A Genetic Algorithm.” Metadata:
- Title: ➤ Discovering Link Communities In Complex Networks By An Integer Programming Model And A Genetic Algorithm.
- Authors: Li, ZhenpingZhang, Xiang-SunWang, Rui-ShengLiu, HongweiZhang, Shihua
- Language: English
Edition Identifiers:
- Internet Archive ID: pubmed-PMC3875478
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The book is available for download in "texts" format, the size of the file-s is: 11.02 Mbs, the file-s for this book were downloaded 67 times, the file-s went public at Fri Oct 24 2014.
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