CCS2021 Satellite Symposium
Data-based Diagnosis of Networked Dynamical Systems

Tiago de Paula Peixoto

Central European University

Network reconstruction from indirect observations
10:20am, Room St Clair 3B

The observed functional behavior of a wide variety 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. We 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.

[1] T.P.P., "Reconstructing networks with unknown and heterogeneous errors", Phys. Rev. X 8 041011 (2018).
[2] T.P.P., "Network reconstruction and community detection from dynamics", Phys. Rev. Lett. 123 128301 (2019).