NETADIS

Statistical Physics Approaches to Networks Across Disciplines

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 Obiettivo del progetto (Objective)

'Networks are ubiquitous in all aspects of human existence. They manifest themselves in everyday life, they underpin the most advanced information and communication technologies, and they provide a powerful framework for addressing a wide spectrum of complex problems in the natural sciences, in engineering, and in economics and the social sciences. Networks, network-related science, and network-based technologies are thus of central importance to maintaining and improving human well-being as well as improving economic, technological, and scientific competitiveness. Statistical physics offers a powerful set of concepts and methods to analyze problems of exactly the type posed by today's key challenges in network science. While the European statistical physics community has an established tradition of coordinated cross-border research collaborations in this area, there has been so far no significant European coordination effort on the initial training side. The proposed ITN is set to fill that gap. Its aim is to train a cadre of future research leaders in advanced methods of analysis, inference, control and optimization of network structure and dynamics, thus maximizing the impact of statistical physics approaches across a broad range of application areas. The ITN will create an innovative training environment by exploiting synergies and complementarities of a research matrix defined by methods and themes on the one hand, and application foci on the other. It will be implemented by targeted secondments of early stage researchers across both axes of the research matrix. Existing local training provision and dedicated network-wide training events will be exploited to ensure that researchers are systematically exposed to the full range of statistical physics techniques and application domains, covered by the 9 leading European research teams and 4 private sector partners integrated into this effort, and will comprise complementary skills training as an integral part.'

Introduzione (Teaser)

The European statistical physics community has established a long tradition of cross-border research collaborations. However, it has not so far acted together in coordinated efforts for young scientists to join research teams and enhance their career prospects.

Descrizione progetto (Article)

With the financial support of the EU, the Initial Training Network (ITN) 'Statistical physics approaches to networks across disciplines' (http://www.netadis.eu/ (NETADIS)) provides the opportunity for young scientists to study the latest applications of statistical physics to network science. It consists of nine leading research institutes and four associated partners from six European countries.

A total of 12 fellows who were registered for PhD degrees have been recruited so far. NETADIS partners organise regular workshops and summer schools for these early-stage researchers (ESRs) to exchange research experiences, insights and new ideas. The largest training event held to date was the first summer school, which was attended by all ESRs and also open to experienced researchers from outside the ITN.

Through a multidisciplinary approach, NETADIS promotes advancing the state of the art in methods for analysis, control and optimisation of networks and their dynamics. The distinguishing feature of research within the NETADIS community is the identification of common traits in complex networks. Its ultimate aim is to maximise the adoption of statistical physics approaches for modelling socio-economy to neurobiological and social networks.

The numerous NETADIS results are described in scientific papers published in international peer-reviewed journals. In addition, research findings that were discussed during the first summer school have been made available on http://videolectures.net/ (VideoLectures.NET), an open access repository of video lectures.

Innovative concepts developed within the NETADIS community have already been included in a White Paper setting priorities for big data research across different fields, from management and marketing to scientific research.