Skip to main content
bioRxiv
  • Home
  • About
  • Submit
  • ALERTS / RSS
Advanced Search
New Results

Exploring community structure in biological networks with random graphs

Pratha Sah, Lisa O. Singh, Aaron Clauset, Shweta Bansal
doi: https://doi.org/10.1101/001545
Pratha Sah
1Department of Biology, Georgetown University, 20057 Washington DC, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Lisa O. Singh
2Department of Computer Science, Georgetown University, 20057 Washington DC, USA.
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Aaron Clauset
3Department of Computer Science, University of Colorado, 80309 Boulder, CO, USA.
4BioFrontiers Institute, University of Colorado, 80303 Boulder, CO, USA.
5Santa Fe Institute, 1399 Hyde Park Rd. 87501 Santa Fe, NM, USA.
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Shweta Bansal
1Department of Biology, Georgetown University, 20057 Washington DC, USA
6Fogarty International Center, National Institutes of Health, 20892 Bethesda, MD, USA.
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • For correspondence: sb753@georgetown.edu
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Supplementary material
  • Preview PDF
Loading

Abstract

A modular pattern, also called community structure, is ubiquitous in biological networks. There has been an increased interest in unraveling the community structure of biological systems as it may provide important insights into a system’s functional components and the impact of local structures on dynamics at a global scale. Choosing an appropriate community detection algorithm to identify the community structure in an empirical network can be difficult, however, as the many algorithms available are based on a variety of cost functions and are difficult to validate. Even when community structure is identified in an empirical system, disentangling the effect of community structure from other network properties such as clustering coefficient and assortativity can be a challenge. Here, we develop a generative model to produce undirected, simple, connected graphs with a specified degrees and pattern of communities, while maintaining a graph structure that is as random as possible. Additionally, we demonstrate two important applications of our model: (a) to generate networks that can be used to benchmark existing and new algorithms for detecting communities in biological networks; and (b) to generate null models to serve as random controls when investigating the impact of complex network features beyond the byproduct of degree and modularity in empirical biological networks. Our model thus allows for the systematic study of the presence of community structure and its impact on network function and dynamics. This process is a crucial step in unraveling the functional consequences of the structural properties of biological systems and uncovering the mechanisms that drive these systems.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY Unported 3.0 license.
Back to top
PreviousNext
Posted December 22, 2013.
Download PDF

Supplementary Material

Email

Thank you for your interest in spreading the word about bioRxiv.

NOTE: Your email address is requested solely to identify you as the sender of this article.

Enter multiple addresses on separate lines or separate them with commas.
Exploring community structure in biological networks with random graphs
(Your Name) has forwarded a page to you from bioRxiv
(Your Name) thought you would like to see this page from the bioRxiv website.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Share
Exploring community structure in biological networks with random graphs
Pratha Sah, Lisa O. Singh, Aaron Clauset, Shweta Bansal
bioRxiv 001545; doi: https://doi.org/10.1101/001545
Reddit logo Twitter logo Facebook logo LinkedIn logo Mendeley logo
Citation Tools
Exploring community structure in biological networks with random graphs
Pratha Sah, Lisa O. Singh, Aaron Clauset, Shweta Bansal
bioRxiv 001545; doi: https://doi.org/10.1101/001545

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
  • Tweet Widget
  • Facebook Like
  • Google Plus One
Subject Areas
All Articles
  • Animal Behavior and Cognition (4838)
  • Biochemistry (10739)
  • Bioengineering (8016)
  • Bioinformatics (27195)
  • Biophysics (13941)
  • Cancer Biology (11086)
  • Cell Biology (15996)
  • Clinical Trials (138)
  • Developmental Biology (8759)
  • Ecology (13246)
  • Epidemiology (2067)
  • Evolutionary Biology (17322)
  • Genetics (11667)
  • Genomics (15887)
  • Immunology (10995)
  • Microbiology (26002)
  • Molecular Biology (10608)
  • Neuroscience (56366)
  • Paleontology (417)
  • Pathology (1729)
  • Pharmacology and Toxicology (2999)
  • Physiology (4530)
  • Plant Biology (9593)
  • Scientific Communication and Education (1610)
  • Synthetic Biology (2672)
  • Systems Biology (6960)
  • Zoology (1507)