# OpenGT: A Comprehensive Benchmark For Graph Transformers

arXiv: https://arxiv.org/abs/2506.04765 · HTML: https://mlsanity.com/paper/2506.04765/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 31 references that are in the map.
- First posted on arXiv 5 Jun 2025.

## Builds on (most cited first, 26 of 26)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Open Graph Benchmark: Datasets for Machine Learning on Graphs](https://mlsanity.com/paper/2005.00687/) (2020)
- [Pitfalls of Graph Neural Network Evaluation](https://mlsanity.com/paper/1811.05868/) (2018)
- [Predict then Propagate: Graph Neural Networks meet Personalized PageRank](https://mlsanity.com/paper/1810.05997/) (2018)
- [Geom-GCN: Geometric Graph Convolutional Networks](https://mlsanity.com/paper/2002.05287/) (2020)
- [Recipe for a General, Powerful, Scalable Graph Transformer](https://mlsanity.com/paper/2205.12454/) (2022)
- [A Generalization of Transformer Networks to Graphs](https://mlsanity.com/paper/2012.09699/) (2020)
- [Rethinking Graph Transformers with Spectral Attention](https://mlsanity.com/paper/2106.03893/) (2021)
- [The Impact of Positional Encoding on Length Generalization in Transformers](https://mlsanity.com/paper/2305.19466/) (2023)
- [Long Range Graph Benchmark](https://mlsanity.com/paper/2206.08164/) (2022)
- [NodeFormer: A Scalable Graph Structure Learning Transformer for Node Classification](https://mlsanity.com/paper/2306.08385/) (2023)
- [Design Space for Graph Neural Networks](https://mlsanity.com/paper/2011.08843/) (2020)
- [Exphormer: Sparse Transformers for Graphs](https://mlsanity.com/paper/2303.06147/) (2023)
- [Graph Inductive Biases in Transformers without Message Passing](https://mlsanity.com/paper/2305.17589/) (2023)
- [SGFormer: Simplifying and Empowering Transformers for Large-Graph Representations](https://mlsanity.com/paper/2306.10759/) (2023)
- [Specformer: Spectral Graph Neural Networks Meet Transformers](https://mlsanity.com/paper/2303.01028/) (2023)
- [Equivariant and Stable Positional Encoding for More Powerful Graph Neural Networks](https://mlsanity.com/paper/2203.00199/) (2022)
- [A Generalization of ViT/MLP-Mixer to Graphs](https://mlsanity.com/paper/2212.13350/) (2022)
- [DIFFormer: Scalable (Graph) Transformers Induced by Energy Constrained Diffusion](https://mlsanity.com/paper/2301.09474/) (2023)
- [GEMSEC: Graph Embedding with Self Clustering](https://mlsanity.com/paper/1802.03997/) (2018)
- [Rethinking Positional Encoding](https://mlsanity.com/paper/2107.02561/) (2021)
- [Graph Transformers: A Survey](https://mlsanity.com/paper/2407.09777/) (2024)
- [Benchmarking Positional Encodings for GNNs and Graph Transformers](https://mlsanity.com/paper/2411.12732/) (2024)
- [Graph Triple Attention Network: A Decoupled Perspective](https://mlsanity.com/paper/2408.07654/) (2024)

## Built on by (most cited first, 2 of 2)

- [Exploring Graph-Transformer Out-of-Distribution Generalization Abilities](https://mlsanity.com/paper/2506.20575/) (2025)
- [HopFormer: Sparse Graph Transformers with Explicit Receptive Field Control](https://mlsanity.com/paper/2602.02268/) (2026)

## Abstract (arXiv)

Graph Transformers (GTs) have recently demonstrated remarkable performance across diverse domains. By leveraging attention mechanisms, GTs are capable of modeling long-range dependencies and complex structural relationships beyond local neighborhoods. However, their applicable scenarios are still underexplored, this highlights the need to identify when and why they excel. Furthermore, unlike GNNs, which predominantly rely on message-passing mechanisms, GTs exhibit a diverse design space in areas such as positional encoding, attention mechanisms, and graph-specific adaptations. Yet, it remains unclear which of these design choices are truly effective and under what conditions. As a result, the community currently lacks a comprehensive benchmark and library to promote a deeper understanding and further development of GTs. To address this gap, this paper introduces OpenGT, a comprehensive benchmark for Graph Transformers. OpenGT enables fair comparisons and multidimensional analysis by establishing standardized experimental settings and incorporating a broad selection of state-of-the-art GNNs and GTs. Our benchmark evaluates GTs from multiple perspectives, encompassing diverse tasks and datasets with varying properties. Through extensive experiments, our benchmark has uncovered several critical insights, including the difficulty of transferring models across task levels, the limitations of local attention, the efficiency trade-offs in several models, the application scenarios of specific positional encodings, and the preprocessing overhead of some positional encodings. We aspire for this work to establish a foundation for future graph transformer research emphasizing fairness, reproducibility, and generalizability. We have developed an easy-to-use library OpenGT for training and evaluating existing GTs. The benchmark code is available at https://github.com/eaglelab-zju/OpenGT.