Plain Transformers Can be Powerful Graph Learners

arXiv:2504.12588 · cs.LG, cs.CL · Submitted 2025-04-17 · Read on arXiv

cs.LG, cs.CL

Submitted: 2025-04-17

Updated: 2026-08-26

Terminology

• Transformer — 164× in this paper · explained in 2 episodes
• GNNs — 55× in this paper · explained in 2 episodes
• Benchmark — 38× in this paper · explained in 2 episodes
• Attention Mechanism — 23× in this paper · explained in 3 episodes
• Transformer Architecture — 19× in this paper · explained in 2 episodes
• Accuracy — 11× in this paper · explained in 1 episode
• Batch Size — 11× in this paper
• Benchmarking — 8× in this paper · explained in 3 episodes
• Positional Encodings — 7× in this paper
• Powerful Plain Graph Transformers — 7× in this paper
• Foundation Model — 6× in this paper · explained in 3 episodes
• Foundation Models — 6× in this paper · explained in 7 episodes
• Adam — 4× in this paper · explained in 2 episodes
• Aggregation — 4× in this paper · explained in 1 episode
• Long-range Graph Benchmark — 4× in this paper
• Ablation — 3× in this paper · explained in 1 episode
• Embeddings — 3× in this paper · explained in 2 episodes
• Generalization — 3× in this paper · explained in 13 episodes
• Ablation study — 2× in this paper · explained in 1 episode
• Alignment — 2× in this paper · explained in 1 episode
• Over-squashing — 2× in this paper · explained in 3 episodes
• AdamW Optimizer — 1× in this paper · explained in 1 episode
• Adjacency Matrix — 1× in this paper · explained in 1 episode
• Anti-Spectral-Bias with Sinusoidal PE Enhancement — 1× in this paper
• Core — 1× in this paper · explained in 2 episodes
• Cosine Similarity — 1× in this paper · explained in 3 episodes
• Covariate Shift — 1× in this paper · explained in 2 episodes
• Diffusion Model — 1× in this paper · explained in 8 episodes
• Diffusion Models — 1× in this paper · explained in 6 episodes
• Distillation — 1× in this paper · explained in 2 episodes
• Feature Extraction — 1× in this paper · explained in 2 episodes
• Generalizability — 1× in this paper · explained in 6 episodes
• Multimodal Learning — 1× in this paper · explained in 2 episodes
• Over-smoothing — 1× in this paper · explained in 4 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Representation Learning — 1× in this paper · explained in 2 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Semi-supervised Learning — 1× in this paper · explained in 4 episodes
• Sparse Attention — 1× in this paper · explained in 5 episodes
• Transfer Learning — 1× in this paper · explained in 10 episodes
• Transformer Models — 1× in this paper · explained in 2 episodes

Sources

• Comparing Graph Transformers via Positional Encodings
• Residual Gated Graph ConvNets
• The Llama 3 Herd of Models
• GRPE: Relative Positional Encoding for Graph Transformer

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