We propose a Markov chain simulation method to generate simple connected
random graphs with a specified degree sequence and level of clustering. The
networks generated by our algorithm are random in all other respects and can
thus serve as generic models for studying the impacts of degree distributions
and clustering on dynamical processes as well as null models for detecting
other structural properties in empirical networks

A Vazquez

A Wagner

C C Friedel

C Gkantsidis

D Gale

D Watts

E Volz

F Harary

F Radicchi

G Robins

J Dunne

J Guillaume

J Montoya

LA Meyers

Lauren Ancel Meyers

M Boguna

M Faloutsos

M Girvan

M Molloy

M Serrano

MEJ Newman

MJ Keeling

N Kashtan

ND Martinez

P Erdos

P Holme

P Trapman

P Yodzis

R Albert

R Milo

R Taylor

RE Ulanowicz

S Bansal

S Goodreau

S Maslov

S Soffer

S V Ricard V Sol

Shashank Khandelwal

Shweta Bansal

SL Pimm

T Petermann

T Snijders

U Gophna

V Colizza

Y Artzy-Randrup

BMC Bioinformatics

English

arXiv

Springer - Publisher Connector

Exploring biological network structure with clustered random networks

Lauren Meyers

Complex biological systems are often modeled as networks of interacting units. Networks of biochemical interactions among proteins, epidemiological contacts among hosts, and trophic interactions in ecosystems, to name a few, have provided useful insights into the dynamical processes that shape and traverse these systems. The degrees of nodes (numbers of interactions) and the extent of clustering (the tendency for a set of three nodes to be interconnected) are two of many well-studied network properties that can fundamentally shape a system. Disentangling the interdependent effects of the various network properties, however, can be difficult. Simple network models can help us quantify the structure of empirical networked systems and understand the impact of various topological properties on dynamics. Results: Here we develop and implement a new Markov chain simulation algorithm to generate simple, connected random graphs that have a specified degree sequence and level of clustering, but are random in all other respects. The implementation of the algorithm (ClustRNet: Clustered Random Networks) provides the generation of random graphs optimized according to a local or global, and relative or absolute measure of clustering. We compare our algorithm to other similar methods and show that ours more successfully produces desired network characteristics. Finding appropriate null models is crucial in bioinformatics research, and is often difficult, particularly for biological networks. As we demonstrate, the networks generated by ClustRNet can serve as random controls when investigating the impacts of complex network features beyond the byproduct of degree and clustering in empirical networks. Conclusion: ClustRNet generates ensembles of graphs of specified edge structure and clustering. These graphs allow for systematic study of the impacts of connectivity and redundancies on network function and dynamics. This process is a key step in unraveling the functional consequences of the structural properties of empirical biological systems and uncovering the mechanisms that drive these systems.University of Texas at AustinMcDonnell FoundationNSF DEB-0749097Integrative Biolog

Bansal, Shweta

Khandelwal, Shashank

Meyers, Lauren Ancel

Texas ScholarWorks

Exploring Biological Network Structure with Clustered Random Networks

Crossref

Shweta Bansal is with the Center for Infectious Disease Dynamics, Penn State University, University Park, PA 16802, USA, and Fogarty International Center, National Institutes of Health, Bethesda, MD 20892, USA, -- Shashank Khandelwal and Lauren Ancel Meyers are with the Section of Integrative Biology, University of Texas at Austin, Austin, TX 78712, USA and areExternal Faculty, Santa Fe Institute, Santa Fe, NM 87501, USABackground: Complex biological systems are often modeled as networks of interacting units. Networks of biochemical interactions among proteins, epidemiological contacts among hosts, and trophic interactions in ecosystems, to name a few, have provided useful insights into the dynamical processes that shape and traverse these systems. The degrees of nodes (numbers of interactions) and the extent of clustering (the tendency for a set of three nodes to be interconnected) are two of many well-studied network properties that can fundamentally shape a system. Disentangling the interdependent effects of the various network properties, however, can be difficult. Simple network models can help us quantify the structure of empirical networked systems and understand the impact of various topological properties on dynamics.
Results: Here we develop and implement a new Markov chain simulation algorithm to generate simple, connected random graphs that have a specified degree sequence and level of clustering, but are random in all other respects. The implementation of the algorithm (ClustRNet: Clustered Random Networks) provides the generation of random graphs optimized according to a local or global, and relative or absolute measure of clustering. We compare our algorithm to other similar methods and show that ours more successfully produces desired network characteristics.

Finding appropriate null models is crucial in bioinformatics research, and is often difficult, particularly for biological networks. As we demonstrate, the networks generated by ClustRNet can serve as random controls when investigating the impacts of complex network features beyond the byproduct of degree and clustering in empirical networks.
Conclusion: ClustRNet generates ensembles of graphs of specified edge structure and clustering. These graphs allow for systematic study of the impacts of connectivity and redundancies on network function and dynamics. This process is a key step in unraveling the functional consequences of the structural properties of empirical biological systems and uncovering the mechanisms that drive these systemsIntegrative Biologyshweta@sbansal.co

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https://bmcbioinformatics.biomedcentral.com/track/pdf/10.1186/1471-2105-10-405?site=bmcbioinformatics.biomedcentral.com