GDBN: a Graph Neural Network Approach to Dynamic Bayesian Network

• URL: http://arxiv.org/abs/2302.10804v1
• Date: Sat, 28 Jan 2023 02:49:13 GMT
• Title: GDBN: a Graph Neural Network Approach to Dynamic Bayesian Network
• Authors: Yang Sun and Yifan Xie
• Abstract summary: We propose a graph neural network approach with score-based method aiming at learning a sparse DAG. We demonstrate methods with graph neural network significantly outperformed other state-of-the-art methods with dynamic bayesian networking inference.
• Score: 7.876789380671075
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Identifying causal relations among multi-variate time series is one of the most important elements towards understanding the complex mechanisms underlying the dynamic system. It provides critical tools for forecasting, simulations and interventions in science and business analytics. In this paper, we proposed a graph neural network approach with score-based method aiming at learning a sparse DAG that captures the causal dependencies in a discretized time temporal graph. We demonstrate methods with graph neural network significantly outperformed other state-of-the-art methods with dynamic bayesian networking inference. In addition, from the experiments, the structural causal model can be more accurate than a linear SCM discovered by the methods such as Notears.

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