A critical look at the evaluation of GNNs under heterophily: Are we really making progress?

Node classification is a classical graph machine learning task on which Graph Neural Networks (GNNs) have recently achieved strong results. However, it is often believed that standard GNNs only work well for homophilous graphs, i.e., graphs where edges tend to connect nodes of the same class. Graphs without this property are called heterophilous, and it is typically assumed that specialized methods are required to achieve strong performance on such graphs. In this work, we challenge this assumption. First, we show that the standard datasets used for evaluating heterophily-specific models have serious drawbacks, making results obtained by using them unreliable. The most significant of these drawbacks is the presence of a large number of duplicate nodes in the datasets squirrel and chameleon, which leads to train-test data leakage. We show that removing duplicate nodes strongly affects GNN performance on these datasets. Then, we propose a set of heterophilous graphs of varying properties that we believe can serve as a better benchmark for evaluating the performance of GNNs under heterophily. We show that standard GNNs achieve strong results on these heterophilous graphs, almost always outperforming specialized models. Our datasets and the code for reproducing our experiments are available at https://github.com/yandex-research/heterophilous-graphs . ISSUES WITH POPULAR HETEROPHILOUS DATASETS In this section, we revisit datasets commonly used for heterophilous node classification. As discussed in Section 2, the following six datasets are the most popular: Wikipedia networks squirrel and chameleon, actor co-occurrence in Wikipedia pages network (actor), and WebKB datasets texas, wisconsin, and cornell. The standard preprocessing of these datasets is done by Pei et al. (2020) . First, we note that these datasets only come from three sources; thus, they