Diego Garlaschelli Group - Econophysics and Network Theory

We study the structure, dynamics and physics of complex networks emerging from the intricate interconnectedness of the constituents of large systems. 

Complex networks naturally emerge in financial, economic, social, neural and biological systems. We combine a theoretical approach, largely based on statistical physics, information theory, discrete mathematics and complexity science, with a data science approach informed by the empirical properties of real-world networks.

Given the strong interdisciplinarity of our research, we regularly collaborate with experts in other fields, especially mathematics, computer science, economics, finance and neuroscience.

Our research interests include: 

• the statistical physics of systems for which the fundamental assumption of ensemble equivalence is broken by the presence of local constraints
• the mathematical modelling of complex networks via maximum-entropy ensembles of random graphs with prescribed properties
• the design of renormalisation schemes for the analysis of networks at multiple scales
• the reconstruction of financial networks from partial information and the reliable estimation of systemic risk from privacy-limited data
•  the detection of early-warning signals of upcoming instabilities in financial systems
• the analysis and modelling of economic networks with nontrivial topology
• the construction of null models of complex systems for statistical pattern detection
• the identification of mesoscopic levels of organization in neural and biological systems from empirical time series and expression profiles
• the refinement of traditional information-theoretic bounds on data compression for large data structures with heterogeneous properties