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- W2017202214 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." @default.
- W2017202214 created "2016-06-24" @default.
- W2017202214 creator A5031952531 @default.
- W2017202214 creator A5046943453 @default.
- W2017202214 date "2006-11-01" @default.
- W2017202214 modified "2023-09-30" @default.
- W2017202214 title "Bipartite graphs as models of complex networks" @default.
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- W2017202214 doi "https://doi.org/10.1016/j.physa.2006.04.047" @default.
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