To analyse the structure of networks in techno-economic segments (TES) characterizing emerging industries (photonics, space industry, ecc), we propose a methodology combining (a) Infomap Multilayer analysis to detect communities; (b) semantic cluster analysis of technologies; (c) text analysis of co-occurrences of location of activities. Our focus is on overlapping communities of agents resulting from FP7 and H2020 programmes consortia, patent application development and ownership, publications.
The main questions to which the proposed methodology could allow to answer concern: (i) How can we single out techno-economic segments (TES) characterizing emerging industries (such as, photonics, space industry, etc.)?; (ii) How can we detect the topics characterizing TES; (iii) Who are the core players in those TES? i.e. types of agents (university, PRO, business company, …), their scientific/technical location, their spatial location (innovation ecosystems); (iv) What are the core activities enhancing the emergence of TES?; (v) How can we analyse the dynamics of formation and change of those networks?
The value added of the multilayer analysis is that it makes possible the analysis of community structure in each TES, and to single out the contribution of each agent (or groups of agents), of each layer and of the detected communities to the generation of the total Infomap flow. Clustering of semantic networks supports a categorization of new topics.
All these aspects can be investigated in their spatial (i.e. geographical) dimension. For instance, both agents centrality and multiple affiliation to communities could be affected by characteristics of the eco systems in which the agents are active. These characteristics, observed at city and regional level, are expected to drive spatial concentrations of specific typologies of agents, hence the spatial distribution of their relationships, thus adding a significant contribution to policy makers.
The paper presents some methodological results of the research activity explored in collaboration with the EC JRC Digital Economy Unit B6 Information Society Unit– in the EU project PREDICT (Prospective Insights on R&D in ICT)1.
1 The views expressed are those of the authors and may not in any circumstances be regarded as stating an official position of the European Commission. Neither the European Commission nor any person acting on behalf of the Commission is responsible for the use which might be made of this abstract.
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