Cooperation patterns in the ERASMUS student exchange network: an empirical study

Abstract

The ERASMUS program is the most extensive cooperation network of European higher education institutions. The network involves 90% of European universities and hundreds of thousands of students. The allocated money and number of travelers in the program are growing yearly. By considering the interconnection of institutions, the study asks how the program’s budget performs, whether the program can achieve its expected goals, and how the program contributes to the development of a European identity, interactions among young people from different countries and learning among cultures. Our goal was to review and explore the elements of network structures that can be used to understand the complexity of the whole ERASMUS student mobility network at the institutional level. The results suggest some socioeconomic and individual behavioral factors underpinning the emergence of the network. While the nodes are spatially distributed, geographical distance does not play a role in the network’s structure, although parallel travelling strategies exist, i.e., in terms of preference of short- and long-distance. The European regions of home and host countries also affect the network. One of the most considerable driving forces of edge formation between institutions are the subject areas represented by participating institutions. The study finds that faculties of institutions are connected rather than institutions, and multilayer network model suggested to explore the mechanisms of those connections. The results indicate that the information uncovered by the study is helpful to scholars and policymakers.

Finding multifaceted communities in multiplex networks

“…This consideration reflects the fact that connectivity is determined by the strength of the vertices, and the modularity function seeks connectivity surpluses regarding the null network, in the case of the vertex degree/strength. In spatial networks, nodes are geographically distributed, and the probability of links between them decreases with geographical distance 7 , except if the connection cost is financed by a third party 38 . The authors used null models considering spatial cost to uncover spatially independent communities 9 , 39 .…”