Self-organization of collaboration networks

TL;DR

This paper introduces a bipartite graph model for collaboration networks that captures their evolving structure and reproduces key topological features without parameter fitting.

Contribution

It presents a simple, parameter-free model combining preferential attachment and bipartite structure to explain collaboration network properties.

Findings

Model accurately reproduces degree distribution and clustering coefficients.

Explains local clustering dependence on degree and degree correlations.

Highlights the role of collaborator aging in network topology.

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.