Graph WaveNet for Deep Spatial-Temporal Graph Modeling

Zonghan Wu · Shirui Pan · Guodong Long · Jing Bo Jiang · Chengqi Zhang

3,706 citations0/0 files storedInternational Joint Conference on Artificial Intelligence

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Traffic Prediction and Management Techniques Spatial and Panel Data Analysis Advanced Graph Neural Networks

Abstract

Spatial-temporal graph modeling is an important task to analyze the spatial relations and temporal trends of components in a system. Existing approaches mostly capture the spatial dependency on a fixed graph structure, assuming that the underlying relation between entities is pre-determined. However, the explicit graph structure (relation) does not necessarily reflect the true dependency and genuine relation may be missing due to the incomplete connections in the data. Furthermore, existing methods are ineffective to capture the temporal trends as the RNNs or CNNs employed in these methods cannot capture long-range temporal sequences. To overcome these limitations, we propose in this paper a novel graph neural network architecture, {Graph WaveNet}, for spatial-temporal graph modeling. By developing a novel adaptive dependency matrix and learn it through node embedding, our model can precisely capture the hidden spatial dependency in the data. With a stacked dilated 1D convolution component whose receptive field grows exponentially as the number of layers increases, Graph WaveNet is able to handle very long sequences. These two components are integrated seamlessly in a unified framework and the whole framework is learned in an end-to-end manner. Experimental results on two public traffic network datasets, METR-LA and PEMS-BAY, demonstrate the superior performance of our algorithm.

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References

2018Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting 2017Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting 2018Deep Multi-View Spatial-Temporal Network for Taxi Demand Prediction

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2020GMAN: A Graph Multi-Attention Network for Traffic Prediction 2020Spatial-Temporal Synchronous Graph Convolutional Networks: A New Framework for Spatial-Temporal Network Data Forecasting 2022Graph neural network for traffic forecasting: A survey 2020Adaptive Graph Convolutional Recurrent Network for Traffic Forecasting 2021Spatial-Temporal Fusion Graph Neural Networks for Traffic Flow Forecasting 2021Learning Dynamics and Heterogeneity of Spatial-Temporal Graph Data for Traffic Forecasting 2023PDFormer: Propagation Delay-Aware Dynamic Long-Range Transformer for Traffic Flow Prediction 2021Spatial-Temporal Graph ODE Networks for Traffic Flow Forecasting

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