Learning the Graph and the Embedding Together: Classifier-Independent Rewiring for Heterophilic Node Classification

arXiv:2609.32613 · cs.LG · Submitted 2026-09-26 · Read on arXiv

cs.LG

Submitted: 2026-09-26

Updated: 2026-09-26

Code: https://github.com/yandex-research/heterophilous-graphs

Terminology

• Accuracy — 71× in this paper · explained in 1 episode
• Embeddings — 30× in this paper · explained in 2 episodes
• Actor — 29× in this paper · explained in 3 episodes
• AUC — 16× in this paper · explained in 2 episodes
• Modularity — 10× in this paper · explained in 9 episodes
• Benchmark — 8× in this paper · explained in 2 episodes
• GNNs — 8× in this paper · explained in 2 episodes
• In International Conference on Learning Representations — 8× in this paper
• Ablation — 7× in this paper · explained in 1 episode
• Over-squashing — 6× in this paper · explained in 3 episodes
• Pseudo-labels — 6× in this paper · explained in 2 episodes
• F1 Score — 5× in this paper · explained in 3 episodes
• Representation Learning — 5× in this paper · explained in 2 episodes
• Fine-Tuning — 4× in this paper · explained in 5 episodes
• Clustering — 3× in this paper · explained in 2 episodes
• Spectral Gap — 3× in this paper · explained in 5 episodes
• Adam — 2× in this paper · explained in 2 episodes
• Adjacency Matrix — 2× in this paper · explained in 1 episode
• Aggregation — 2× in this paper · explained in 1 episode
• Contrastive Learning — 2× in this paper · explained in 5 episodes
• In International Conference on Machine Learning — 2× in this paper
• Over-smoothing — 2× in this paper · explained in 4 episodes
• Adversarial Attacks — 1× in this paper · explained in 1 episode
• Graph Neural Networks (GNNs) — 1× in this paper · explained in 7 episodes
• Heterogeneous Graph — 1× in this paper · explained in 2 episodes
• Linear Probing — 1× in this paper · explained in 2 episodes
• Semi-supervised Learning — 1× in this paper · explained in 4 episodes

Sources

• Make Heterophily Graphs Better Fit GNN: A Graph Rewiring Approach
• Deep Graph Contrastive Representation Learning

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