On an Efficient Marie Curie Initial Training Network

TL;DR

This paper investigates an algorithm for constructing efficient collaboration networks in Marie Curie Initial Training Networks, aiming to optimize researcher mobility and network structure for enhanced scientific collaboration.

Contribution

It introduces a novel algorithm that promotes star-like network expansion, improving the design of collaboration networks in research training programs.

Findings

Efficient network expansion results in star-like structures.

The algorithm enhances collaboration efficiency.

Insights can inform future network proposals.

Abstract

Collaboration in science is one of the key components of world-class research. The European Commission supports collaboration between institutions and funds young researchers appointed by these partner institutions. In these networks, the mobility of the researchers is enforced in order to enhance the collaboration. In this study, based on a real Marie Curie Initial Training Network, an algorithm to construct a collaboration network is investigated. The algorithm suggests that a strongly efficient expansion leads to a star-like network. The results might help the design of efficient collaboration networks for future Initial Training Network proposals.