Naples | 16-17 May 2017
Margherita Russo presents the paper "Clustering and network analysis of techno-economic segments characterizing emergent", by Margherita Russo, Annalisa Caloffi, Federica Rossi and Pasquale Pavone.
ARS'17 International Workshop
SPECIAL SESSION 3 Innovation Networks: The Complex Relations of Actors, Organizations and Industries
Clustering and Network Analysis of Techno-Economic Segments Characterizing Emergent Industries
by Margherita Russo, University of Modena and Reggio, Italy Annalisa Caloffi, Università di Padua, Italy Federica Rossi, Birkbeck University, UK Pasquale Pavone, University of Modena and Reggio, Italy
Abstract To analyse the structure of networks in techno-economic segments (TES) characterizing emerging industries (photonics, space industry, ecc), we will focus on overlapping communities of agents resulting from FP7 and H2020 programmes consortia, patent application development and ownership, business structures ownership, situation of ownership and control in general (ie HQ controlling some research centres), merges and acquisitions, belonging to communities of different types. Communities will be detected by applying Infomap multilayer analysis. The main questions to which this methodology could allow to answer concern: (i) the analysis of community structure in each TES; (ii) the role of agents involved (by individuals and by category) with respect to the whole network, the individual layers and the detected communities. With regard to agents' centrality, multilayer Infomap flow is strongly correlated to eigenvector centrality and to other network centrality measures: all these measures are embedded in the structure of the network. The value added of the multilayer analysis is that it makes possible 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. All these aspects can are 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 CLUES that will be further explored in collaboration with the EU "JRC B6 Digital Economy" unit ? in the EU project PREDICT (Prospective Insights on R&D in ICT).