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Graph Neural Networks: Expressivity vs. Generalization Explored

By PulseAugur Editorial · [1 sources] ·

A new paper explores the relationship between the expressivity of Graph Neural Networks (GNNs) and their generalization capabilities. Researchers introduced a set of premetrics to quantify structural similarities between graphs, linking these to the performance of expressive GNNs. The findings suggest that while more expressive GNNs can perform better, they may generalize poorly unless their increased complexity is offset by larger training datasets or reduced differences between training and testing data. AI

IMPACT Provides theoretical grounding for understanding GNN performance, potentially guiding future model development.

RANK_REASON The cluster contains an academic paper detailing theoretical insights and empirical results on graph neural networks. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Graph Neural Networks: Expressivity vs. Generalization Explored

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The cluster contains an academic paper detailing theoretical insights and empirical results on graph neural networks. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Sohir Maskey, Raffaele Paolino, Fabian Jogl, Gitta Kutyniok, Johannes F. Lutzeyer ·

    Graph Representational Learning: When Does More Expressivity Hurt Generalization?

    arXiv:2505.11298v2 Announce Type: replace Abstract: Graph Neural Networks (GNNs) are powerful tools for learning on structured data, yet the relationship between their expressivity and predictive performance remains unclear. We introduce a family of premetrics that capture differ…

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