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Measuring Structure in Graph Benchmark Datasets Using Graph Invariants
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-
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