Literature DB >> 16089694

Network clustering coefficient without degree-correlation biases.

Sara Nadiv Soffer1, Alexei Vázquez.   

Abstract

The clustering coefficient quantifies how well connected are the neighbors of a vertex in a graph. In real networks it decreases with the vertex degree, which has been taken as a signature of the network hierarchical structure. Here we show that this signature of hierarchical structure is a consequence of degree-correlation biases in the clustering coefficient definition. We introduce a definition in which the degree-correlation biases are filtered out, and provide evidence that in real networks the clustering coefficient is constant or decays logarithmically with vertex degree.

Year:  2005        PMID: 16089694     DOI: 10.1103/PhysRevE.71.057101

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


  35 in total

Review 1.  Resting developments: a review of fMRI post-processing methodologies for spontaneous brain activity.

Authors:  Daniel S Margulies; Joachim Böttger; Xiangyu Long; Yating Lv; Clare Kelly; Alexander Schäfer; Dirk Goldhahn; Alexander Abbushi; Michael P Milham; Gabriele Lohmann; Arno Villringer
Journal:  MAGMA       Date:  2010-10-24       Impact factor: 2.310

2.  Extracting the hierarchical organization of complex systems.

Authors:  Marta Sales-Pardo; Roger Guimerà; André A Moreira; Luís A Nunes Amaral
Journal:  Proc Natl Acad Sci U S A       Date:  2007-09-19       Impact factor: 11.205

3.  Predictors of coupling between structural and functional cortical networks in normal aging.

Authors:  Rafael Romero-Garcia; Mercedes Atienza; Jose L Cantero
Journal:  Hum Brain Mapp       Date:  2013-09-12       Impact factor: 5.038

4.  Hierarchical functional modularity in the resting-state human brain.

Authors:  Luca Ferrarini; Ilya M Veer; Evelinda Baerends; Marie-José van Tol; Remco J Renken; Nic J A van der Wee; Dirk J Veltman; André Aleman; Frans G Zitman; Brenda W J H Penninx; Mark A van Buchem; Johan H C Reiber; Serge A R B Rombouts; Julien Milles
Journal:  Hum Brain Mapp       Date:  2009-07       Impact factor: 5.038

5.  Using network properties to predict disease dynamics on human contact networks.

Authors:  Gregory M Ames; Dylan B George; Christian P Hampson; Andrew R Kanarek; Cayla D McBee; Dale R Lockwood; Jeffrey D Achter; Colleen T Webb
Journal:  Proc Biol Sci       Date:  2011-04-27       Impact factor: 5.349

Review 6.  Graph analysis of functional brain networks: practical issues in translational neuroscience.

Authors:  Fabrizio De Vico Fallani; Jonas Richiardi; Mario Chavez; Sophie Achard
Journal:  Philos Trans R Soc Lond B Biol Sci       Date:  2014-10-05       Impact factor: 6.237

7.  Characterizing the correlations between local phase fractions of gas-liquid two-phase flow with wire-mesh sensor.

Authors:  C Tan; W L Liu; F Dong
Journal:  Philos Trans A Math Phys Eng Sci       Date:  2016-06-28       Impact factor: 4.226

8.  Exploratory analysis of protein translation regulatory networks using hierarchical random graphs.

Authors:  Daniel D Wu; Xiaohua Hu; E K Park; Xiaofeng Wang; Jiali Feng; Xindong Wu
Journal:  BMC Bioinformatics       Date:  2010-04-29       Impact factor: 3.169

9.  The function of communities in protein interaction networks at multiple scales.

Authors:  Anna C F Lewis; Nick S Jones; Mason A Porter; Charlotte M Deane
Journal:  BMC Syst Biol       Date:  2010-07-22

10.  Exploring biological network structure with clustered random networks.

Authors:  Shweta Bansal; Shashank Khandelwal; Lauren Ancel Meyers
Journal:  BMC Bioinformatics       Date:  2009-12-09       Impact factor: 3.169

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