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Algorithms for generating large-scale clustered random graphs

Published online by Cambridge University Press:  21 November 2014

CHENG WANG ,
OMAR LIZARDO  and
DAVID HACHEN
CHENG WANG
Affiliation:
Department of Population Health and Disease Prevention, University of California, Irvine, A.I.R.Bldg 653, Suite 2040H, 653 East Peltason Drive, Irvine, CA 92697, USA (e-mail: wang.cheng@uci.edu)
OMAR LIZARDO
Affiliation:
Department of Sociology, University of Notre Dame, 735 Flanner, Notre Dame, IN 46545, USA (e-mail: olizardo@nd.edu, dhachen@nd.edu)
DAVID HACHEN
Affiliation:
Department of Sociology, University of Notre Dame, 735 Flanner, Notre Dame, IN 46545, USA (e-mail: olizardo@nd.edu, dhachen@nd.edu)
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Abstract

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Real-world networks are often compared to random graphs to assess whether their topological structure could be a result of random processes. However, a simple random graph in large scale often lacks social structure beyond the dyadic level. As a result we need to generate clustered random graph to compare the local structure at higher network levels. In this paper a generalized version of Gleeson's algorithm G(VS, VT, ES, ET, S, T) is advanced to generate a clustered random graph in large-scale which persists the number of vertices |V|, the number of edges |E|, and the global clustering coefficient CΔ as in the real network and it works successfully for nine large-scale networks. Our new algorithm also has advantages in randomness evaluation and computation efficiency when compared with the existing algorithms.

Keywords

large-scale networkclustered random graphgenerating algorithm

Information

Type
Research Article
Information
Network Science , Volume 2 , Issue 3 , December 2014 , pp. 403 - 415
Copyright
Copyright © Cambridge University Press 2014 

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