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Title: Recovering the Graph Underlying Networked Dynamical Systems under Partial Observability: A Deep Learning Approach
We study the problem of graph structure identification, i.e., of recovering the graph of dependencies among time series. We model these time series data as components of the state of linear stochastic networked dynamical systems. We assume partial observability, where the state evolution of only a subset of nodes comprising the network is observed. We propose a new feature-based paradigm: to each pair of nodes, we compute a feature vector from the observed time series. We prove that these features are linearly separable, i.e., there exists a hyperplane that separates the cluster of features associated with connected pairs of nodes from those of disconnected pairs. This renders the features amenable to train a variety of classifiers to perform causal inference. In particular, we use these features to train Convolutional Neural Networks (CNNs). The resulting causal inference mechanism outperforms state-of-the-art counterparts w.r.t. sample-complexity. The trained CNNs generalize well over structurally distinct networks (dense or sparse) and noise-level profiles. Remarkably, they also generalize well to real-world networks while trained over a synthetic network -- namely, a particular realization of a random graph.  more » « less
Award ID(s):
2327905
PAR ID:
10576827
Author(s) / Creator(s):
; ; ; ; ;
Publisher / Repository:
PKP Publishing Services Network
Date Published:
Journal Name:
Proceedings of the AAAI Conference on Artificial Intelligence
Volume:
37
Issue:
7
ISSN:
2159-5399
Page Range / eLocation ID:
9038 to 9046
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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