TNA, a stacked GCN-GRU model with variational sampling, predicts new edges in temporal graphs and outperforms baselines on three real datasets.
Interaction networks for learning about objects, relations and physics,
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Temporal Neighbourhood Aggregation: Predicting Future Links in Temporal Graphs via Recurrent Variational Graph Convolutions
TNA, a stacked GCN-GRU model with variational sampling, predicts new edges in temporal graphs and outperforms baselines on three real datasets.