3 accepted papers
Benchmark datasets have proved pivotal to the success of graph learning, and *good* benchmark datasets are crucial to guide the development of the field. Recent research has highlighted problems with graph-learning datasets and benchmarking practices—revealing, for example, that methods which ignore…
Echoing recent calls to counter reliability and robustness concerns in machine learning via *multiverse analysis*, we present PRESTO, a principled framework for *mapping the multiverse* of machine-learning models that rely on *latent representations*. Although such models enjoy widespread adoption,…
Graph generative model evaluation necessitates understanding differences between graphs on the distributional level. This entails being able to harness salient attributes of graphs in an efficient manner. Curvature constitutes one such property of graphs, and has recently started to prove useful in…