Clustering and preferential attachment in growing networks

TL;DR

This paper empirically investigates how collaboration networks in physics and biology grow, showing that the likelihood of new collaborations increases with shared collaborators and past collaborations, supporting mechanisms behind clustering and power-law distributions.

Contribution

It provides empirical evidence for the roles of clustering and preferential attachment in the evolution of scientific collaboration networks.

Findings

Collaboration probability increases with shared collaborators.

New collaborators are more likely for scientists with many past collaborators.

Results support mechanisms for clustering and power-law degree distributions.

Abstract

We study empirically the time evolution of scientific collaboration networks in physics and biology. In these networks, two scientists are considered connected if they have coauthored one or more papers together. We show that the probability of scientists collaborating increases with the number of other collaborators they have in common, and that the probability of a particular scientist acquiring new collaborators increases with the number of his or her past collaborators. These results provide experimental evidence in favor of previously conjectured mechanisms for clustering and power-law degree distributions in networks.