Bipartite graphs as models of complex networks

Jean-Loup Guillaume and Matthieu Latapy

Physica A: Statistical Mechanics and its Applications, 2006, vol. 371, issue 2, 795-813

Abstract: It appeared recently that the classical random graph model used to represent real-world complex networks does not capture their main properties. Since then, various attempts have been made to provide accurate models. We study here a model which achieves the following challenges: it produces graphs which have the three main wanted properties (clustering, degree distribution, average distance), it is based on some real-world observations, and it is sufficiently simple to make it possible to prove its main properties. This model consists in sampling a random bipartite graph with prescribed degree distribution. Indeed, we show that any complex network may be viewed as a bipartite graph with some specific characteristics, and that its main properties may be viewed as consequences of this underlying structure. We also propose a growing model based on this observation.

Keywords: Complex networks; Bipartite graphs; Affiliation networks; Clustering; Modeling (search for similar items in EconPapers)
Date: 2006
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Citations: View citations in EconPapers (9)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:371:y:2006:i:2:p:795-813

DOI: 10.1016/j.physa.2006.04.047

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