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Graph Neural Networks (GNNs) have achieved great success on a nodeclassification task. Despite the broad interest in developing and evaluatingGNNs, they have been assessed with limited benchmark datasets. As a result, theexisting evaluation of GNNs lacks fine-grained analysis from variouscharacteristics of graphs. Motivated by this, we conduct extensive experimentswith a synthetic graph generator that can generate graphs having controlledcharacteristics for fine-grained analysis. Our empirical studies clarify thestrengths and weaknesses of GNNs from four major characteristics of real-worldgraphs with class labels of nodes, i.e., 1) class size distributions (balancedvs. imbalanced), 2) edge connection proportions between classes (homophilic vs.heterophilic), 3) attribute values (biased vs. random), and 4) graph sizes(small vs. large). In addition, to foster future research on GNNs, we publiclyrelease our codebase that allows users to evaluate various GNNs with variousgraphs. We hope this work offers interesting insights for future research.

Beyond Real-world Benchmark Datasets: An Empirical Study of Node Classification with GNNs