When Design Rules Break: Benchmark Composition Determines Whether Label Informativeness Predicts GNN Aggregator Choice
arXiv:2606.10249v1 Announce Type: new Abstract: We examine whether graph neural network (GNN) design rules generalize across benchmark families by studying aggregator selection (sum, mean, max) on 24 node-classification datasets spanning citation, heterophilic, LINKX Facebook-100, co-purchase, and co-authorship graphs. Edge homophily is only weakly predictive of the GIN-Sum versus GIN-Mean performance gap. Label informativeness predicts this gap well on legacy benchmarks but degrades substantially when Facebook-100 graphs are included. In these dense friendship networks, near-zero label inform
The rapid acceleration of AI research necessitates constant re-evaluation of fundamental design principles in light of new and diverse datasets. This paper published on arXiv reflects the ongoing efforts to refine graph neural network architectures.
This research provides critical insights into the generalizability of GNN design rules, directly impacting the robustness and reliability of AI models, particularly in complex social and economic systems. It highlights the potential for foundational assumptions to break down when applied to new data domains.
Our understanding of GNN aggregator selection is refined, pushing researchers to consider dataset characteristics, like dense friendship networks, more deeply when designing appropriate architectures. The findings challenge the universal applicability of existing design guidelines.
- · AI researchers and developers
- · Companies building GNN-based products
- · Graph analytics platforms
- · Over-generalized GNN design paradigms
Further research will focus on developing more adaptive GNN architectures that perform consistently across diverse graph types.
Improved GNN reliability will enable more robust applications in areas like social network analysis, recommendation systems, and drug discovery.
The enhanced predictive power of GNNs could lead to breakthroughs in understanding complex interdependencies in areas like financial markets or geopolitical influence.
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Read at arXiv cs.LG