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Self-organization of collaboration networks.
José J Ramasco1, S N Dorogovtsev, Romualdo Pastor-Satorras
1Departamento de Física and Centro de Física do Porto, Faculdade de Ciências, Universidade do Parto, Rua do Campo Alegre 687, 4169-007 Porto, Portugal. jjramasc@fc.up.pt
Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics
|November 5, 2004
Summary
We developed a growing network model for collaboration networks. This model explains key network features like degree distribution and clustering without needing parameter fitting.
Area of Science:
- Network Science
- Graph Theory
- Sociology of Science
Background:
- Collaboration networks are fundamental in many scientific fields.
- Understanding their structure and evolution is crucial for analyzing scientific progress.
Purpose of the Study:
- To propose a novel evolving, self-organizing bipartite graph model for collaboration networks.
- To explain key topological characteristics of collaboration networks using basic network properties.
Main Methods:
- Developed a growing network model combining preferential edge attachment and bipartite structure.
- Analyzed one-mode projections of real collaboration networks.
Main Results:
- The model successfully describes topological characteristics like degree distribution and clustering coefficient without parameter fitting.
- Explained local clustering dependence on degree and degree-degree correlations.
Conclusions:
- The proposed model offers a parsimonious explanation for observed network properties.
- Collaborator 'aging' and collaboration limits are key factors in network evolution.