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Found 4 papers, 3 papers with code
Tackling Graph Oversquashing by Global and Local Non-Dissipativity
A common problem in Message-Passing Neural Networks is oversquashing -- the limited ability to facilitate effective information flow between distant nodes.
Temporal Graph ODEs for Irregularly-Sampled Time Series
Modern graph representation learning works mostly under the assumption of dealing with regularly sampled temporal graph snapshots, which is far from realistic, e. g., social networks and physical systems are characterized by continuous dynamics and sporadic observations.
Deep learning for dynamic graphs: models and benchmarks
Recent progress in research on Deep Graph Networks (DGNs) has led to a maturation of the domain of learning on graphs.
Anti-Symmetric DGN: a stable architecture for Deep Graph Networks
Deep Graph Networks (DGNs) currently dominate the research landscape of learning from graphs, due to their efficiency and ability to implement an adaptive message-passing scheme between the nodes.
