Research map / Graph learning / GNN architectures

Size Transferability of Graph Convolutional Networks across Sparsity: A Generalized Graphon Perspective

ICML 2026 · Graph learning › GNN architectures · approach: Theory of graph convolutional networks

Read the paperCheck your idea against 108,967 papers

AI-extracted summary. Written by AI from the paper's title only (no summary yet). Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.

Closest papers by meaning

• Graphon Neural Networks and the Transferability of Graph Neural Networks (NeurIPS 2020 · similarity 0.91)
• Deeper Insights Into Graph Convolutional Networks for Semi-Supervised Learning (AAAI 2018 · similarity 0.90)
• On Provable Benefits of Depth in Training Graph Convolutional Networks (NeurIPS 2021 · similarity 0.90)
• A Manifold Perspective on the Statistical Generalization of Graph Neural Networks (ICML 2025 · similarity 0.90)
• On the Stability of Graph Convolutional Neural Networks: A Probabilistic Perspective (NeurIPS 2025 · similarity 0.89)
• Higher-Order Graphon Neural Networks: Approximation and Cut Distance (ICLR 2025 · similarity 0.88)