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Open Access Highly Accessed Methodology article

Exploring community structure in biological networks with random graphs

Pratha Sah1, Lisa O Singh2, Aaron Clauset345 and Shweta Bansal16*

Author Affiliations

1 Department of Biology, Georgetown University, 20057 Washington DC, USA

2 Department of Computer Science, Georgetown University, 20057 Washington DC, USA

3 Department of Computer Science, University of Colorado, 80309 Boulder, CO, USA

4 BioFrontiers Institute, University of Colorado, 80303 Boulder, CO, USA

5 Santa Fe Institute, 1399 Hyde Park Rd., 87501 Santa Fe, NM, USA

6 Fogarty International Center, National Institutes of Health, 20892 Bethesda, MD, USA

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BMC Bioinformatics 2014, 15:220  doi:10.1186/1471-2105-15-220

Published: 25 June 2014

Abstract

Background

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.

Results

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.

Conclusion

Our model 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.

Keywords:
Biological networks; Community structure; Random graphs; Modularity; Benchmark graphs