Literature DB >> 15524586

Self-organization of collaboration networks.

José J Ramasco1, S N Dorogovtsev, Romualdo Pastor-Satorras.   

Abstract

We study collaboration networks in terms of evolving, self-organizing bipartite graph models. We propose a model of a growing network, which combines preferential edge attachment with the bipartite structure, generic for collaboration networks. The model depends exclusively on basic properties of the network, such as the total number of collaborators and acts of collaboration, the mean size of collaborations, etc. The simplest model defined within this framework already allows us to describe many of the main topological characteristics (degree distribution, clustering coefficient, etc.) of one-mode projections of several real collaboration networks, without parameter fitting. We explain the observed dependence of the local clustering on degree and the degree-degree correlations in terms of the "aging" of collaborators and their physical impossibility to participate in an unlimited number of collaborations.

Year:  2004        PMID: 15524586     DOI: 10.1103/PhysRevE.70.036106

Source DB:  PubMed          Journal:  Phys Rev E Stat Nonlin Soft Matter Phys        ISSN: 1539-3755


  12 in total

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4.  Collaboration networks from a large CV database: dynamics, topology and bonus impact.

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5.  Evolution of gossip-based indirect reciprocity on a bipartite network.

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Journal:  Sci Rep       Date:  2016-11-25       Impact factor: 4.379

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7.  Fractal and multifractal analyses of bipartite networks.

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8.  Evolutionary dynamics of group interactions on structured populations: a review.

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Journal:  J R Soc Interface       Date:  2013-01-09       Impact factor: 4.118

9.  Driving forces of researchers mobility.

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Journal:  Sci Rep       Date:  2014-05-07       Impact factor: 4.379

10.  Emergence of Multiplex Communities in Collaboration Networks.

Authors:  Federico Battiston; Jacopo Iacovacci; Vincenzo Nicosia; Ginestra Bianconi; Vito Latora
Journal:  PLoS One       Date:  2016-01-27       Impact factor: 3.240

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