Detecting structural perturbations from time series with deep learning

• URL: http://arxiv.org/abs/2006.05232v1
• Date: Tue, 9 Jun 2020 13:08:40 GMT
• Title: Detecting structural perturbations from time series with deep learning
• Authors: Edward Laurence, Charles Murphy, Guillaume St-Onge, Xavier Roy-Pomerleau, and Vincent Thibeault
• Abstract summary: We present a graph neural network approach to infer structural perturbations from functional time series. We show our data-driven approach outperforms typical reconstruction methods. This work uncovers a practical avenue to study the resilience of real-world complex systems.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Small disturbances can trigger functional breakdowns in complex systems. A challenging task is to infer the structural cause of a disturbance in a networked system, soon enough to prevent a catastrophe. We present a graph neural network approach, borrowed from the deep learning paradigm, to infer structural perturbations from functional time series. We show our data-driven approach outperforms typical reconstruction methods while meeting the accuracy of Bayesian inference. We validate the versatility and performance of our approach with epidemic spreading, population dynamics, and neural dynamics, on various network structures: random networks, scale-free networks, 25 real food-web systems, and the C. Elegans connectome. Moreover, we report that our approach is robust to data corruption. This work uncovers a practical avenue to study the resilience of real-world complex systems.

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