Université de Montréal

This work fine-tunes baseline models on 27 widely used benchmark datasets, categorizes heterophilic datasets into malignant, benign, and ambiguous groups based on whether graph-aware models underperform their coupled graph-agnostic counterparts, re-evaluates 11 SOTA heterophily-specific models, and conducts the first quantitative evaluation of 11 homophily metrics on synthetic graphs from three generation methods, finding that most SOTA models do not significantly outperform the best baselines and that classic metrics remain competitive.

This work fine-tunes baseline models on 27 widely used benchmark datasets, categorizes heterophilic datasets into malignant, benign, and ambiguous groups based on whether graph-aware models underperform their coupled graph-agnostic counterparts, re-evaluates 11 SOTA heterophily-specific models, and conducts the first quantitative evaluation of 11 homophily metrics on synthetic graphs from three generation methods, finding that most SOTA models do not significantly outperform the best baselines and that classic metrics remain competitive.