Literature DB >> 21929067

Map equation for link communities.

Youngdo Kim1, Hawoong Jeong.   

Abstract

Community structure exists in many real-world networks and has been reported being related to several functional properties of the networks. The conventional approach was partitioning nodes into communities, while some recent studies start partitioning links instead of nodes to find overlapping communities of nodes efficiently. We extended the map equation method, which was originally developed for node communities, to find link communities in networks. This method is tested on various kinds of networks and compared with the metadata of the networks, and the results show that our method can identify the overlapping role of nodes effectively. The advantage of this method is that the node community scheme and link community scheme can be compared quantitatively by measuring the unknown information left in the networks besides the community structure. It can be used to decide quantitatively whether or not the link community scheme should be used instead of the node community scheme. Furthermore, this method can be easily extended to the directed and weighted networks since it is based on the random walk.

Mesh:

Year:  2011        PMID: 21929067     DOI: 10.1103/PhysRevE.84.026110

Source DB:  PubMed          Journal:  Phys Rev E Stat Nonlin Soft Matter Phys        ISSN: 1539-3755


  11 in total

1.  An edge-centric perspective on the human connectome: link communities in the brain.

Authors:  Marcel A de Reus; Victor M Saenger; René S Kahn; Martijn P van den Heuvel
Journal:  Philos Trans R Soc Lond B Biol Sci       Date:  2014-10-05       Impact factor: 6.237

2.  Overlapping community detection in networks based on link partitioning and partitioning around medoids.

Authors:  Alexander Ponomarenko; Leonidas Pitsoulis; Marat Shamshetdinov
Journal:  PLoS One       Date:  2021-08-25       Impact factor: 3.240

3.  Identifying overlapping and hierarchical thematic structures in networks of scholarly papers: a comparison of three approaches.

Authors:  Frank Havemann; Jochen Gläser; Michael Heinz; Alexander Struck
Journal:  PLoS One       Date:  2012-03-27       Impact factor: 3.240

4.  Identification of hybrid node and link communities in complex networks.

Authors:  Dongxiao He; Di Jin; Zheng Chen; Weixiong Zhang
Journal:  Sci Rep       Date:  2015-03-02       Impact factor: 4.379

5.  Link community detection using generative model and nonnegative matrix factorization.

Authors:  Dongxiao He; Di Jin; Carlos Baquero; Dayou Liu
Journal:  PLoS One       Date:  2014-01-28       Impact factor: 3.240

6.  Combined node and link partitions method for finding overlapping communities in complex networks.

Authors:  Di Jin; Bogdan Gabrys; Jianwu Dang
Journal:  Sci Rep       Date:  2015-02-26       Impact factor: 4.379

7.  Discovering communities in complex networks by edge label propagation.

Authors:  Wei Liu; Xingpeng Jiang; Matteo Pellegrini; Xiaofan Wang
Journal:  Sci Rep       Date:  2016-03-01       Impact factor: 4.379

8.  Micro-blog user community discovery using generalized SimRank edge weighting method.

Authors:  Jinshan Qi; Xun Liang; Xiaoping Zhou; Zhiyu Li; Yu Liu; Hengchao Cheng
Journal:  PLoS One       Date:  2018-05-07       Impact factor: 3.240

9.  Mobile recommendation based on link community detection.

Authors:  Kun Deng; Jianpei Zhang; Jing Yang
Journal:  ScientificWorldJournal       Date:  2014-08-26

10.  Inverse Resolution Limit of Partition Density and Detecting Overlapping Communities by Link-Surprise.

Authors:  Juyong Lee; Zhong-Yuan Zhang; Jooyoung Lee; Bernard R Brooks; Yong-Yeol Ahn
Journal:  Sci Rep       Date:  2017-09-29       Impact factor: 4.379

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