Temporal Graph Networks combine memory modules and graph operators to learn on dynamic graphs as timed event sequences, outperforming prior methods on transductive and inductive tasks while unifying earlier models as special cases.
Diffusion convolutional recurrent neural network: Data-driven traffic forecasting
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Graph State-Space Models jointly learn state-space dynamics and latent relational graphs end-to-end from time series for forecasting and structure extraction.
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Temporal Graph Networks for Deep Learning on Dynamic Graphs
Temporal Graph Networks combine memory modules and graph operators to learn on dynamic graphs as timed event sequences, outperforming prior methods on transductive and inductive tasks while unifying earlier models as special cases.
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Graph State-Space Models and Latent Relational Inference
Graph State-Space Models jointly learn state-space dynamics and latent relational graphs end-to-end from time series for forecasting and structure extraction.