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Forecasting future states of sensors is key to solving tasks like weather prediction, route planning, and many others when dealing with networks of sensors.
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Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting
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Inductive representation learning on large graphs
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Neural message passing for quantum chemistry
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Graph attention networks
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Attention is all you need
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Luxembourg SUMO Traffic (LuST) Scenario: Traffic Demand Evaluation
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Rethinking the faster r-cnn architecture for temporal action localization
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Kriging convolutional networks
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Eta prediction with graph neural networks in google maps
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Graph neural network for traffic forecasting: A survey
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A review of graph neural networks and their applications in power systems
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PyTorch Geometric Temporal: Spatiotemporal Signal Processing with Neural Machine Learning Models
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Graph neural networks in network neuroscience
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How attentive are graph attention networks?
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