Revealing the Pitfalls and Re-Evaluating the Advancement of Heterophilic Graph Learning

Understand

Over the past decade, Graph Neural Networks (GNNs) have achieved great success on machine learning tasks with relational data.

• However, recent studies have found that heterophily can cause significant performance degradation of GNNs, especially on node-level tasks.
• Numerous heterophilic benchmark datasets have been put forward to validate the efficacy of heterophily-specific GNNs, and various homophily metrics have been designed to help recognize these challenging datasets.
• Nevertheless, there still exist multiple pitfalls that severely hinder the proper evaluation of new models and metrics: 1) lack of hyperparameter tuning; 2) insufficient evaluation on the truly challenging heterophilic datasets; 3) missing quantitative evaluation for homophily metrics on synthetic graphs.