Plain Transformers are Surprisingly Powerful Link Predictors

📰 ArXiv cs.AI

Learn how plain Transformers can be used for link prediction in graph machine learning, challenging traditional Graph Neural Network approaches

advanced Published 1 Jun 2026

Action Steps

Implement a plain Transformer model for link prediction using PyTorch or TensorFlow
Preprocess graph data into node and edge representations
Train the Transformer model on the graph data to learn topological dependencies
Evaluate the model's performance using metrics such as accuracy and AUC-ROC
Compare the results with traditional GNN-based approaches to assess the effectiveness of the Transformer model

Who Needs to Know This

Data scientists and machine learning engineers working on graph-based projects can benefit from this knowledge to improve their link prediction models

Key Insight

💡 Plain Transformers can capture complex topological dependencies in graphs without relying on explicit structural heuristics or memory-intensive node embeddings

Key Takeaways

Learn how plain Transformers can be used for link prediction in graph machine learning, challenging traditional Graph Neural Network approaches

Full Article

Title: Plain Transformers are Surprisingly Powerful Link Predictors

Abstract:
arXiv:2602.01553v2 Announce Type: replace-cross Abstract: Link prediction is a core challenge in graph machine learning, demanding models that capture rich and complex topological dependencies. While Graph Neural Networks (GNNs) are the standard solution, state-of-the-art pipelines often rely on explicit structural heuristics or memory-intensive node embeddings -- approaches that struggle to generalize or scale to massive graphs. Emerging Graph Transformers (GTs) offer a potential alternative bu

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