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International audience ; This paper analyses the influence of proximity on the evolution of collaboration networks. It determines empirically how organizations choose their partners according to their geographical, cognitive, organizational, institutional and social proximity. Relational databases are constructed from R&D collaborative projects, funded under the European Union 6th Framework Programme within the navigation by satellite industry (GNSS) from 2004 to 2007. The stochastic actor-based model SIENA is used to model the network dynamic as a realisation of a continuous-time Markov chain and to estimate parameters for underlying mechanisms of its evolution. Empirical results show that geographical, organizational and institutional proximity favour collaborations, while cognitive and social proximity do not play a significant role.

International audience ; This paper analyses the influence of proximity on the evolution of collaboration networks. It determines empirically how organizations choose their partners according to their geographical, cognitive, organizational, institutional and social proximity. Relational databases are constructed from R&D collaborative projects, funded under the European Union 6th Framework Programme within the navigation by satellite industry (GNSS) from 2004 to 2007. The stochastic actor-based model SIENA is used to model the network dynamic as a realisation of a continuous-time Markov chain and to estimate parameters for underlying mechanisms of its evolution. Empirical results show that geographical, organizational and institutional proximity favour collaborations, while cognitive and social proximity do not play a significant role.

L'article étudie les contributions de vingt-cinq années de recherche sur les dynamiques de proximité au regard de leurs apports sur les fondements et l'analyse du changement régional. Il considère que, malgré des avancées importantes, il demeure des zones d'ombre sur l'articulation entre les déterminants microéconomiques de la formation des liens de proximité et les dynamiques macro-régionales. L'article montre que l'analyse des propriétés structurelles des réseaux permet d'éclairer cette articulation et réduire le gap observé entre les recherches micro et macro-fondées des dynamiques de proximité. En associant les dynamiques de changement structurel des régions à l'évolution de l'architecture de leurs réseaux, il suggère de rechercher les micro-fondements de ces dynamiques dans l'articulation de trois registres distincts de proximité. Les perspectives ouvertes dans cet article sont discutées dans le cadre de trois programmes de recherche apparus ces dernières années (résilience régionale, smart specialization, politiques de cluster). ; The paper focuses on the contributions of twenty-five years of research on proximity dynamics in terms of their contribution to the foundations and analysis of structural change at the reginal level. It considers that, despite significant progress, there are still shadow zones in the link between the microeconomic determinants of the formation of proximity links and macro-regional dynamics. The article shows that the analysis of the structural properties of the networks makes it possible to clarify this articulation and to reduce the observed gap between micro and macro-based researches of proximity dynamics. By associating the dynamics of structural change of the regions with the evolution of the architecture of their networks, it suggests to look for the micro-foundations of these dynamics in the articulation of three distinct registers of proximity. The perspectives opened are discussed in the context of three research programs that have emerged in recent years (regional resilience, smart specialization, cluster policies).

A key objective of the EU Framework Programmes for Research and Innovation is the creation of cross-country research networks. We make use of Social Network tools to describe the evolution of the EU research network across countries on the basis of unique data covering collaborative projects launched during the first four years of implementation of Horizon 2020 and its predecessor programmes, the Sixth and Seventh Framework Programme. We describe the positioning of all EU-countries in the collaborative research network, the positioning of the older member EU-15 and the newer member EU-13 countries in particular, and to what extent the network has been subject to change during the period 2003–2017. EU-15 and EU-13 countries have become more integrated, and some organizations fulfil a bridging function in the EU research network. EU-13 countries are more heavily engaged in parts of the programme on lower complexity research activities.