Bringing Citizens, Models and Data together in Participatory, Interactive SociaL Exploratories

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We propose visionary research to develop modeling, computational, and ICT tools needed to predict and influence disease spread and other contagion phenomena in complex social systems. To achieve non-incremental advances we will combine large scale, realistic, data-driven models with participatory data-collection and advanced methods for Big Data analysis. In particular we will go beyond the one-dimensional focus of current approaches tackling one aspect of the problem at a time. We will interconnect contagion progression (e.g. epidemics) with social adaptation, the economic impact and other systemic aspects that will finally allow a complete analysis of the inherent systemic risk.

We will develop models dealing with multiple time and length scales simultaneously, leading to the definition of new, layered computational approaches. Towards policy impact and social response we will work to close the loop between models, data, behavior and perception and develop new concepts for the explanation, visualization and interaction with data and models both on individual and on collective level. We will cast the fundamental advances into an integrated system building on widely accepted open ICT technologies that will be used and useful beyond the project.

As a tangible ICT outcome directed at facilitating the uptake and impact of the project, we will implement “Interactive Social Exploratories” defined as interactive environments which act as a front-end to a set of parameterizable and adjustable models, data analysis techniques, visualization methods and data collection frameworks.

In summary, we aim to:

  • Produce fundamental theoretical, methodological and technological advances
  • Mold them into a broadly usable ICT platform that will be a catalyst for producing, delivering, and embedding scientific evidence into the policy and societal processes and
  • Evaluate the system empirically with policy makers and citizens focusing on the concrete problem of epidemic spreading.
Milli, L., G. Rossetti, D. Pedreschi, and F. Giannotti, "Diffusive Phenomena in Dynamic Networks: a data-driven study", International Conference on Complex Networks CompleNet, Boston March 5-8 2018, Springer, 2018.
Rossetti, G., L. Milli, S. Rinzivillo, A. Sirbu, D. Pedreschi, and F. Giannotti, "NDlib: a python library to model and analyze diffusion processes over complex networks", International Journal of Data Science and Analytics, vol. 5, no. 1, pp. 61–79, 2018.
Milli, L., G. Rossetti, D. Pedreschi, and F. Giannotti, "Information diffusion in complex networks: The active/passive conundrum", International Workshop on Complex Networks and their Applications: Springer, 2017.
Rossetti, G., L. Milli, S. Rinzivillo, A. Sirbu, D. Pedreschi, and F. Giannotti, "NDlib: Studying Network Diffusion Dynamics", IEEE International Conference on Data Science and Advanced Analytics, DSA, Tokyo, 2017.
Rossetti, G., L. Pappalardo, and S. Rinzivillo, "A novel approach to evaluate community detection algorithms on ground truth", 7th Workshop on Complex Networks, Dijon, France, Springer-Verlag, 2016.
Guidotti, R., A. Monreale, S. Rinzivillo, D. Pedreschi, and F. Giannotti, "Unveiling mobility complexity through complex network analysis", Social Network Analysis and Mining, vol. 6, no. 1, pp. 59, 2016.


Web Site
Start Date
1 January 2015
End Date
31 December 2017
European Project
Department of Computer Science, University of Pisa (DI-UNIPI)
Istituto di Scienza e Tecnologie dell’Informazione, National Research Council of Italy (ISTI-CNR)