Literature DB >> 17677523

Partitioning and modularity of graphs with arbitrary degree distribution.

Jörg Reichardt1, Stefan Bornholdt.   

Abstract

We solve the graph bipartitioning problem in dense graphs with arbitrary degree distribution using the replica method. We find the cut size to scale universally with <square root k> . In contrast, earlier results studying the problem in graphs with a Poissonian degree distribution had found a scaling with square root <k> [Fu and Anderson, J. Phys. A 19, 1605 (1986)]. Our results also generalize to the problem of q partitioning. They can be used to find the expected modularity Q [Newman and Girvan, Phys. Rev. E 69, 026113 (2004)] of random graphs and allow for the assessment of the statistical significance of the output of community detection algorithms.

Year:  2007        PMID: 17677523     DOI: 10.1103/PhysRevE.76.015102

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


  9 in total

1.  To cut or not to cut? Assessing the modular structure of brain networks.

Authors:  Yu-Teng Chang; Dimitrios Pantazis; Richard M Leahy
Journal:  Neuroimage       Date:  2014-01-15       Impact factor: 6.556

2.  Neonatal neural networks predict children behavioral profiles later in life.

Authors:  Chong-Yaw Wee; Ta Anh Tuan; Birit F P Broekman; Min Yee Ong; Yap-Seng Chong; Kenneth Kwek; Lynette Pei-Chi Shek; Seang-Mei Saw; Peter D Gluckman; Marielle V Fortier; Michael J Meaney; Anqi Qiu
Journal:  Hum Brain Mapp       Date:  2016-11-16       Impact factor: 5.038

3.  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

4.  Finding and testing network communities by lumped Markov chains.

Authors:  Carlo Piccardi
Journal:  PLoS One       Date:  2011-11-03       Impact factor: 3.240

5.  BinTree seeking: a novel approach to mine both bi-sparse and cohesive modules in protein interaction networks.

Authors:  Qing-Ju Jiao; Yan-Kai Zhang; Lu-Ning Li; Hong-Bin Shen
Journal:  PLoS One       Date:  2011-11-28       Impact factor: 3.240

6.  Inferring meaningful communities from topology-constrained correlation networks.

Authors:  Jose Sergio Hleap; Christian Blouin
Journal:  PLoS One       Date:  2014-11-19       Impact factor: 3.240

7.  Resolving anatomical and functional structure in human brain organization: identifying mesoscale organization in weighted network representations.

Authors:  Christian Lohse; Danielle S Bassett; Kelvin O Lim; Jean M Carlson
Journal:  PLoS Comput Biol       Date:  2014-10-02       Impact factor: 4.475

8.  Hamiltonian energy as an efficient approach to identify the significant key regulators in biological networks.

Authors:  Shazia Haider; Kalaiarasan Ponnusamy; R K Brojen Singh; Anirban Chakraborti; Rameshwar N K Bamezai
Journal:  PLoS One       Date:  2019-08-23       Impact factor: 3.240

9.  On the role of sparseness in the evolution of modularity in gene regulatory networks.

Authors:  Carlos Espinosa-Soto
Journal:  PLoS Comput Biol       Date:  2018-05-18       Impact factor: 4.475

  9 in total

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