Measuring Structure in Graph Benchmark Datasets Using Graph Invariants

Excerpt

arXiv:2605.06462v2 Announce Type: replace Abstract: Progress in graph learning is hindered by benchmark practices that conflate the contributions of node features and graph structure, making it hard to tell whether a model actually learns from the graph, or whether it even needs to. We propose measuring this using graph invariants, i.e., permutation-invariant, task-agnostic structural descriptors. Our analysis on datasets substantiates three tacit assumptions in graph learning, namely that (i) a