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.
Structured sequence modeling with graph convolutional recurrent networks
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
fields
cs.LG 3verdicts
UNVERDICTED 3representative citing papers
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.
Characterizes an estimation-prediction tradeoff in binary logistic models for causal probabilistic temporal graphs and proposes a framework to jointly evaluate temporal link prediction with causal parameter recovery via Cramér-Rao bounds.
citing papers explorer
-
'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.
-
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.
-
Estimation--Prediction Tradeoff in Causal Probabilistic Temporal Graphs
Characterizes an estimation-prediction tradeoff in binary logistic models for causal probabilistic temporal graphs and proposes a framework to jointly evaluate temporal link prediction with causal parameter recovery via Cramér-Rao bounds.