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.
International Conference on Learning Representations , year=
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GenTTP is a generalized predictor that learns to forecast network-wide travel times and flows for arbitrary route choice distributions rather than only typical daily patterns.
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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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Generalising Travel Time Prediction To Varying Route Choices In Urban Networks
GenTTP is a generalized predictor that learns to forecast network-wide travel times and flows for arbitrary route choice distributions rather than only typical daily patterns.