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.
Related papers
- How neural networks learn to classify chaotic time series [77.34726150561087]
We study the inner workings of neural networks trained to classify regular-versus-chaotic time series.
We find that the relation between input periodicity and activation periodicity is key for the performance of LKCNN models.
arXiv Detail & Related papers (2023-06-04T08:53:27Z) - Dynamic Causal Explanation Based Diffusion-Variational Graph Neural
Network for Spatio-temporal Forecasting [60.03169701753824]
We propose a novel Dynamic Diffusion-al Graph Neural Network (DVGNN) fortemporal forecasting.
The proposed DVGNN model outperforms state-of-the-art approaches and achieves outstanding Root Mean Squared Error result.
arXiv Detail & Related papers (2023-05-16T11:38:19Z) - Graph-Time Convolutional Neural Networks: Architecture and Theoretical
Analysis [12.995632804090198]
We introduce Graph-Time Convolutional Neural Networks (GTCNNs) as principled architecture to aid learning.
The approach can work with any type of product graph and we also introduce a parametric graph to learn also the producttemporal coupling.
Extensive numerical results on benchmark corroborate our findings and show the GTCNN compares favorably with state-of-the-art solutions.
arXiv Detail & Related papers (2022-06-30T10:20:52Z) - Learning the Evolutionary and Multi-scale Graph Structure for
Multivariate Time Series Forecasting [50.901984244738806]
We show how to model the evolutionary and multi-scale interactions of time series.
In particular, we first provide a hierarchical graph structure cooperated with the dilated convolution to capture the scale-specific correlations.
A unified neural network is provided to integrate the components above to get the final prediction.
arXiv Detail & Related papers (2022-06-28T08:11:12Z) - Bayesian Inference of Stochastic Dynamical Networks [0.0]
This paper presents a novel method for learning network topology and internal dynamics.
It is compared with group sparse Bayesian learning (GSBL), BINGO, kernel-based methods, dynGENIE3, GENIE3 and ARNI.
Our method achieves state-of-the-art performance compared with group sparse Bayesian learning (GSBL), BINGO, kernel-based methods, dynGENIE3, GENIE3 and ARNI.
arXiv Detail & Related papers (2022-06-02T03:22:34Z) - Differentiable Reasoning over Long Stories -- Assessing Systematic
Generalisation in Neural Models [12.479512369785082]
We consider two classes of neural models: "E-GNN", the graph-based models that can process graph-structured data and consider the edge attributes simultaneously; and "L-Graph", the sequence-based models which can process linearized version of the graphs.
We found that the modified recurrent neural network yield surprisingly accurate results across every systematic generalisation tasks which outperform the graph neural network.
arXiv Detail & Related papers (2022-03-20T18:34:42Z) - Deep Dynamic Effective Connectivity Estimation from Multivariate Time
Series [0.0]
We develop dynamic effective connectivity estimation via neural network training (DECENNT)
DECENNT outperforms state-of-the-art (SOTA) methods on five different tasks and infers interpretable task-specific dynamic graphs.
arXiv Detail & Related papers (2022-02-04T21:14:21Z) - Learning Neural Causal Models with Active Interventions [83.44636110899742]
We introduce an active intervention-targeting mechanism which enables a quick identification of the underlying causal structure of the data-generating process.
Our method significantly reduces the required number of interactions compared with random intervention targeting.
We demonstrate superior performance on multiple benchmarks from simulated to real-world data.
arXiv Detail & Related papers (2021-09-06T13:10:37Z) - Towards Deeper Graph Neural Networks [63.46470695525957]
Graph convolutions perform neighborhood aggregation and represent one of the most important graph operations.
Several recent studies attribute this performance deterioration to the over-smoothing issue.
We propose Deep Adaptive Graph Neural Network (DAGNN) to adaptively incorporate information from large receptive fields.
arXiv Detail & Related papers (2020-07-18T01:11:14Z) - Learned Factor Graphs for Inference from Stationary Time Sequences [107.63351413549992]
We propose a framework that combines model-based algorithms and data-driven ML tools for stationary time sequences.
neural networks are developed to separately learn specific components of a factor graph describing the distribution of the time sequence.
We present an inference algorithm based on learned stationary factor graphs, which learns to implement the sum-product scheme from labeled data.
arXiv Detail & Related papers (2020-06-05T07:06:19Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.