graphdeeplearning/graphtransformer

Health v2 · maintenance only

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Adoption not part of the score

1028 stars · 150 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

Reference implementation of the Graph Transformer architecture from the paper 'A Generalization of Transformer Networks to Graphs' (DLG-AAAI'21). It adapts transformer attention to arbitrary graphs using Laplacian eigenvector positional encodings, batch normalization, and edge feature representations.

Use cases

• apply transformer architecture to graph-structured data
• run graph neural network experiments with positional encodings
• reproduce results from the graph transformer paper
• benchmark graph transformer models on standard graph datasets
• model molecular graphs with bond-type edge features
• learn how attention mechanisms generalize to graphs
• compare graph transformer against standard GNN baselines

When to choose

• you need a reference implementation of graph transformers with Laplacian positional encodings
• you want to reproduce or extend the DLG-AAAI'21 paper results
• your graph data has meaningful edge features like chemical bond types
• you are researching attention-based models on arbitrary graph topologies

When to avoid

• you need a production-ready, actively maintained GNN library
• you want a general-purpose graph deep learning framework rather than a single paper's code
• you need transformers for sequences or NLP rather than graphs
• you require long-term support or frequent updates, since the repo has not seen releases since 2021

Facets

library · maturity maintenance

deep-learning machine-learning benchmarking machine-learning deep-learning python cross-platform graph-neural-networks graph-transformer attention positional-encoding laplacian-eigenvectors research-code aaai-2021 pytorch molecular-graphs edge-features algorithms research gpu linux

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