Diego Garlaschelli

Diego Garlaschelli

IMT Lucca

Dr Diego Garlaschelli is Associate Professor at the IMT School of Advanced Studies in Lucca (IT), where he directs the Networks research unit, and at the Lorentz Institute for Theoretical Physics of Leiden University (NL), where he leads the Econophysics and Network Theory group. His research interests are strongly interdisciplinary and include network theory, statistical physics, financial complexity, information theory, social dynamics and biological systems. He teaches courses in Complex Networks, Econophysics, and Complex Systems. He holds a 4-year master degree in theoretical physics from the University of Rome III (2001) and a PhD in Physics from the University of Siena (2005). He held postdoctoral positions at the Australian National University in Canberra (Australia), the University of Siena (Italy), the University of Oxford (UK) and the S. Anna School for Advanced Studies in Pisa (Italy). He has given more than 50 invited talks at international conferences, workshops, and scientific schools. He is author of more than 100 publications in peer-reviewed international journals and peer-reviewed book chapters, and of one co-authored monograph.

Network reconstruction from local information

In several disciplines, cascades of failures or distress can propagate across a large networked system, possibly leading to the collapse of a significant number of its components. In order to correctly estimate the risk of such cascades, a detailed knowledge of the structure of the entire network is in principle required. However, due to data limitedness or confidentiality, the network may be largely unobservable, e.g. only aggregate node-specific information may be available. Is it possible to statistically reconstruct the hidden structure of a network and reliably infer its large-scale properties? In this talk, I will present a maximum-entropy approach to the problem of network reconstruction from local information. I will illustrate the power of the method when applied to the inference of network properties and systemic risk in various economic and financial systems. Then, as a counter-example, I will show how in certain circumstances the real network may deviate significantly from its reconstructed counterpart, thereby highlighting anomalous structural patterns. If such anomalies increase systematically over time, they may serve as early-warning signals of approaching critical events.