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Found 3 papers, 2 papers with code
Exphormer: Sparse Transformers for Graphs
We show that incorporating Exphormer into the recently-proposed GraphGPS framework produces models with competitive empirical results on a wide variety of graph datasets, including state-of-the-art results on three datasets.
Ranked #1 on
Graph Classification
on MNIST
Evaluating Graph Generative Models with Contrastively Learned Features
A wide range of models have been proposed for Graph Generative Models, necessitating effective methods to evaluate their quality.
TD-GEN: Graph Generation With Tree Decomposition
We propose TD-GEN, a graph generation framework based on tree decomposition, and introduce a reduced upper bound on the maximum number of decisions needed for graph generation.
