Literature DB >> 34432789

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

Alexander Ponomarenko1, Leonidas Pitsoulis2, Marat Shamshetdinov1.   

Abstract

In this paper, we present a new method for detecting overlapping communities in networks with a predefined number of clusters called LPAM (Link Partitioning Around Medoids). The overlapping communities in the graph are obtained by detecting the disjoint communities in the associated line graph employing link partitioning and partitioning around medoids which are done through the use of a distance function defined on the set of nodes. We consider both the commute distance and amplified commute distance as distance functions. The performance of the LPAM method is evaluated with computational experiments on real life instances, as well as synthetic network benchmarks. For small and medium-size networks, the exact solution was found, while for large networks we found solutions with a heuristic version of the LPAM method.

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Year:  2021        PMID: 34432789      PMCID: PMC8386890          DOI: 10.1371/journal.pone.0255717

Source DB:  PubMed          Journal:  PLoS One        ISSN: 1932-6203            Impact factor:   3.240


  14 in total

Review 1.  Community structure in social and biological networks.

Authors:  M Girvan; M E J Newman
Journal:  Proc Natl Acad Sci U S A       Date:  2002-06-11       Impact factor: 11.205

2.  Finding and evaluating community structure in networks.

Authors:  M E J Newman; M Girvan
Journal:  Phys Rev E Stat Nonlin Soft Matter Phys       Date:  2004-02-26

3.  Uncovering the overlapping community structure of complex networks in nature and society.

Authors:  Gergely Palla; Imre Derényi; Illés Farkas; Tamás Vicsek
Journal:  Nature       Date:  2005-06-09       Impact factor: 49.962

4.  Finding community structure in networks using the eigenvectors of matrices.

Authors:  M E J Newman
Journal:  Phys Rev E Stat Nonlin Soft Matter Phys       Date:  2006-09-11

5.  Near linear time algorithm to detect community structures in large-scale networks.

Authors:  Usha Nandini Raghavan; Réka Albert; Soundar Kumara
Journal:  Phys Rev E Stat Nonlin Soft Matter Phys       Date:  2007-09-11

Review 6.  Maps of random walks on complex networks reveal community structure.

Authors:  Martin Rosvall; Carl T Bergstrom
Journal:  Proc Natl Acad Sci U S A       Date:  2008-01-23       Impact factor: 11.205

7.  Benchmark graphs for testing community detection algorithms.

Authors:  Andrea Lancichinetti; Santo Fortunato; Filippo Radicchi
Journal:  Phys Rev E Stat Nonlin Soft Matter Phys       Date:  2008-10-24

8.  Line graphs, link partitions, and overlapping communities.

Authors:  T S Evans; R Lambiotte
Journal:  Phys Rev E Stat Nonlin Soft Matter Phys       Date:  2009-07-09

9.  Finding statistically significant communities in networks.

Authors:  Andrea Lancichinetti; Filippo Radicchi; José J Ramasco; Santo Fortunato
Journal:  PLoS One       Date:  2011-04-29       Impact factor: 3.240

10.  The ground truth about metadata and community detection in networks.

Authors:  Leto Peel; Daniel B Larremore; Aaron Clauset
Journal:  Sci Adv       Date:  2017-05-03       Impact factor: 14.136

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