  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
