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- 18:00 CRI (online)

Network reconstruction from indirect observations

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Network reconstruction from indirect observations

Happy to invite you for the network seminar with Tiago P. Peixoto. The talk will be online, please register to receive the link!

Network reconstruction from indirect observations

The observed functional behavior of a wide variety of large-scale systems is often the result of a network of pairwise interactions. However, in many cases these interactions are hidden from us, either because they are impossible or very costly to be measured directly, or, in the best case, are measured with some degree of uncertainty. In such situations, we are required to infer the network of interactions from indirect information.

In this talk, I present a scalable Bayesian method to perform network reconstruction from indirect data, including noisy measurements and observed network dynamics. This kind of approach allows us to convey in a principled manner the uncertainty present in the measurement, and combined with versatile modelling assumptions can yield good results even when data are scarce. In particular, I describe how the reconstruction approach can be combined with community detection, allowing us to tap into multiple sources of evidence available for the task. I show how this combined approach provides a twofold improvement, by increasing not only the reconstruction accuracy, but also the identification of communities in networks. The latter improvement is possible even in situations where at first we might imagine that reconstruction is superfluous, for example when direct network data are available and measurement errors can be neglected.

Bio: Tiago P. Peixoto is an Associate Professor at the Department of Network and Data Science at the Central European University https://networkdatascience.ceu.edu/people/tiago-peixoto His research focuses on characterizing, identifying and explaining large-scale patterns found in the structure and function of complex network systems — representing diverse phenomena with physical, biological, technological, or social origins — using principled approaches from statistical physics, nonlinear dynamics and Bayesian inference. Peixoto develops and maintains graph-tool — an efficient Python module for manipulation and statistical analysis of graphs and networks. He has a PhD in Physics from the University of São Paulo, and a Habilitation in Theoretical Physics from the University of Bremen. In 2019 he was the recipient of the Erdős–Rényi Prize in Network Science, awarded by the Network Science Society.


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