SiST-GNN performs simultaneous spatial-temporal message passing on a temporally augmented graph and reports 109-277% gains in fixed-split dynamic link prediction over prior methods.
Rossi, Nesreen K
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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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'Si'multaneous 'S'patial-'T'emporal Message Passing for Dynamic Graph Representation Learning
SiST-GNN performs simultaneous spatial-temporal message passing on a temporally augmented graph and reports 109-277% gains in fixed-split dynamic link prediction over prior methods.
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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.