{"id":39310,"date":"2022-03-01T15:32:21","date_gmt":"2022-03-01T14:32:21","guid":{"rendered":"\/gruppo4\/?page_id=39310"},"modified":"2022-04-21T13:25:25","modified_gmt":"2022-04-21T11:25:25","slug":"lincoln_en","status":"publish","type":"page","link":"\/gruppo4\/en\/lincoln_en\/","title":{"rendered":"Lincoln_en"},"content":{"rendered":"\n

Lincoln<\/span><\/h2>\n

Learning Complex Networks<\/b><\/em><\/span><\/b><\/p>\n\n\n\n

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Local Coordinator:   <\/strong><\/span><\/p>\n

+ Armando Bazzani<\/strong><\/span>

E-Mail: armando.bazzani@bo.infn.it<\/a>
Tel: +39-051-2091198
Site: Irnerio
Office: 159<\/div>\n
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National web site<\/span><\/a><\/span><\/p>\n\n\n\n

The LINCOLN initiative considers a Statistical Mechanics approach to complex systems with applications to biological <\/span>and social systems. In particular, we aim at developing techniques to exploit and control the properties of <\/span>complex networks and the processes living on them. For instance one can make social and technological networks more <\/span>robust and resilient and apply modern statistical techniques to the design and control of natural and <\/span>artificial neural networks. The collaboration involves 6 INFN sections with complementary competences of Theoretical Physics <\/span>and Dynamical Systems.<\/span><\/p>\n

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The Bologna group is focused on statistical and dynamical properties of artificial and real neural networks, the <\/span>interplay between social and information networks and resilience and controllability of neural and ecological networks. <\/span>The applied methodologies are based on Dynamical Systems, Stochastic Processes and Complex Network Theory(ex. nonlinear random walks on graphs and <\/span>dynamical systems coupled through a complex network structure). The main applications are to human mobility, transport models, <\/span>and biochemical and neural networks.<\/span><\/p>\n

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Groups member
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