Analysis of ERASMUS staff and student mobility network within a big European project

Savić, Miloš; Ivanović, Mirjana; Putnik, Zoran; Tutuncu, Kemal; Budimac, Zoran; Smrikarova, Stoyanka; Smrikarov, Angel

doi:10.23919/mipro.2017.7973498

Cited by 6 publications

(2 citation statements)

References 8 publications

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“…A self-respondent-based induced subnetwork of 37 HEIs from 25 countries also shows a core-periphery structure with a densely connected core composed of active institutions (Savić et al 2017). The network of 134330 student exchanges between 2333 HEIs in 2003 was analyzed, and the authors found that the degree distribution of the ERASMUS student network is not scale-free.…”

Section: Network Science-based View Of the Erasmus Networkmentioning

confidence: 99%

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

et al. 2022

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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.

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Section: Network Science-based View Of the Erasmus Networkmentioning

confidence: 99%

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

et al. 2022

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“…Regional and national connections were analyzed by gravity models [4][5][6] and the generalized method of moments 7 to explore the economic background of the connections. The structure of the network was also compared to other collaboration networks to understand the nature of such mobilities 5,8 . Only two analyses focus on the topological properties of contract-and flow-networks of the European HEIs 9,10 .…”

Section: Background and Summarymentioning

confidence: 99%

A multilayer and spatial description of the Erasmus mobility network

et al. 2020

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András telcs 1,5,6,7 & János abonyi 4* the Erasmus Programme is the biggest collaboration network consisting of European Higher Education Institutions (HEIs). The flows of students, teachers and staff form directed and weighted networks that connect institutions, regions and countries. Here, we present a linked and manually verified dataset of this multiplex, multipartite, multi-labelled, spatial network. We enriched the network with institutional socioeconomic data from the European Tertiary Education Register (ETER) and the Global Research Identifier Database (GRID). We geocoded the headquarters of institutions and characterised the attractiveness and quality of their environments based on Points of Interest (POI) data. The linked datasets provide relevant information to grasp a more comprehensive understanding of the mobility patterns and attractiveness of the institutions.

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Collaboration Networks Analysis: Combining Structural and Keyword-Based Approaches

Meštrović¹

2018

Semantic Keyword-Based Search on Structured Data Sources

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Analysis of ERASMUS staff and student mobility network within a big European project

Cited by 6 publications

References 8 publications

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

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

A multilayer and spatial description of the Erasmus mobility network

Collaboration Networks Analysis: Combining Structural and Keyword-Based Approaches

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