Graph Neural Network Design Rules Show Limited Generalization Across Benchmark Families
A new arXiv preprint finds that established design rules for selecting graph neural network aggregators do not generalize uniformly across different benchmark dataset families. The study tested sum, mean, and max aggregators across 24 node-classification datasets and found that label informativeness — a commonly used predictor — loses its predictive power when dense Facebook-100 social network graphs are included. This matters because it suggests widely cited GNN design guidelines may be artifacts of which benchmarks researchers happen to use, rather than universal principles.
Researchers submitted a preprint to arXiv examining whether graph neural network (GNN) aggregator selection rules — specifically choosing between sum, mean, and max aggregation — hold across diverse benchmark families. Testing on 24 node-classification datasets covering citation networks, heterophilic graphs, Facebook-100 friendship networks, co-purchase, and co-authorship graphs, they found that edge homophily is only weakly predictive of aggregator performance gaps. Label informativeness, a more commonly trusted predictor, works well on legacy benchmarks but breaks down when Facebook-100 graphs are included, where near-zero label informativeness paradoxically coexists with strong sum-aggregation advantages of 7–13%. Stochastic block model simulations, including degree-corrected variants, failed to reproduce this behavior, ruling out mean degree as the sole explanation. Instead, the spectral gap of the graph emerged as the key distinguishing statistic for these dense social networks, with the effect localized to one-hop neighborhoods and replicated across multiple architectures. The authors also show that PNA, a multi-aggregator model, can underperform the best single-aggregator GNN on standard citation benchmarks, further complicating conventional wisdom. The findings suggest that benchmark composition — not numerical inadequacy — drives whether design rules appear to generalize, and they propose the Facebook-100 regime as a concrete testbed for future adaptive aggregation research.
What's missing
As a preprint, this work has not yet undergone peer review. The study does not evaluate aggregator behavior on dynamic or temporal graphs, nor does it address scalability to graphs larger than those in the Facebook-100 collection. The proposed role of the spectral gap is correlational and a causal mechanism is not fully established.
What different sources said
- arXiv cs.LGCenter
When Design Rules Break: Benchmark Composition Determines Whether Label Informativeness Predicts GNN Aggregator Choice
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