2 papers
cs.LG2026
OgBench: A Framework for Evaluating Graph Neural Networks on Omics Data
Louisa Cornelis, Johan Mathe, Louis Van Langendonck +2
Graph Neural Networks (GNNs) have become the dominant framework for inductive graph-level learning. Yet most benchmarks focus on the regime , where the number of graphs $n…
cs.LG2026
GraphUniverse: Synthetic Graph Generation for Evaluating Inductive Generalization
Louis Van Langendonck, Guillermo Bernárdez, Nina Miolane +1
A fundamental challenge in graph learning is understanding how models generalize to new, unseen graphs. While synthetic benchmarks offer controlled settings for analysis, existing…