Bipartite Graphs as Models of Complex Networks
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.
Cite
@article{arxiv.cond-mat/0307095,
title = {Bipartite Graphs as Models of Complex Networks},
author = {Jean-Loup Guillaume and Matthieu Latapy},
journal= {arXiv preprint arXiv:cond-mat/0307095},
year = {2021}
}