"graph transformers" Papers

20 papers found

A Closer Look at Graph Transformers: Cross-Aggregation and Beyond

Jiaming Zhuo, Ziyi Ma, Yintong Lu et al.

NEURIPS 2025spotlight

DUALFormer: Dual Graph Transformer

Zhuo Jiaming, Yuwei Liu, Yintong Lu et al.

ICLR 2025
3
citations

GMV: A Unified and Efficient Graph Multi-View Learning Framework

Qipeng zhu, Jie Chen, Jian Pu et al.

NEURIPS 2025

GrokFormer: Graph Fourier Kolmogorov-Arnold Transformers

GUOGUO AI, Guansong Pang, Hezhe Qiao et al.

ICML 2025arXiv:2411.17296
3
citations

Learning Long Range Dependencies on Graphs via Random Walks

Dexiong Chen, Till Schulz, Karsten Borgwardt

ICLR 2025arXiv:2406.03386
20
citations

MOL-Mamba: Enhancing Molecular Representation with Structural & Electronic Insights

Jingjing Hu, Dan Guo, Zhan Si et al.

AAAI 2025paperarXiv:2412.16483
6
citations

Relieving the Over-Aggregating Effect in Graph Transformers

Junshu Sun, Wanxing Chang, Chenxue Yang et al.

NEURIPS 2025arXiv:2510.21267

Rethinking Tokenized Graph Transformers for Node Classification

Jinsong Chen, Chenyang Li, Gaichao Li et al.

NEURIPS 2025arXiv:2502.08101
5
citations

Tokenphormer: Structure-aware Multi-token Graph Transformer for Node Classification

Zijie Zhou, Zhaoqi Lu, Xuekai Wei et al.

AAAI 2025paperarXiv:2412.15302
4
citations

Understanding Virtual Nodes: Oversquashing and Node Heterogeneity

Joshua Southern, Francesco Di Giovanni, Michael Bronstein et al.

ICLR 2025arXiv:2405.13526
11
citations

Aligning Transformers with Weisfeiler-Leman

Luis Müller, Christopher Morris

ICML 2024arXiv:2406.03148
6
citations

CKGConv: General Graph Convolution with Continuous Kernels

Liheng Ma, Soumyasundar Pal, Yitian Zhang et al.

ICML 2024arXiv:2404.13604
9
citations

Comparing Graph Transformers via Positional Encodings

Mitchell Black, Zhengchao Wan, Gal Mishne et al.

ICML 2024arXiv:2402.14202
24
citations

Graph Positional and Structural Encoder

Semih Cantürk, Renming Liu, Olivier Lapointe-Gagné et al.

ICML 2024arXiv:2307.07107
30
citations

Less is More: on the Over-Globalizing Problem in Graph Transformers

Yujie Xing, Xiao Wang, Yibo Li et al.

ICML 2024arXiv:2405.01102

On the Expressive Power of Spectral Invariant Graph Neural Networks

Bohang Zhang, Lingxiao Zhao, Haggai Maron

ICML 2024arXiv:2406.04336
18
citations

Recurrent Distance Filtering for Graph Representation Learning

Yuhui Ding, Antonio Orvieto, Bobby He et al.

ICML 2024arXiv:2312.01538
13
citations

Subgraphormer: Unifying Subgraph GNNs and Graph Transformers via Graph Products

Guy Bar Shalom, Beatrice Bevilacqua, Haggai Maron

ICML 2024arXiv:2402.08450
11
citations

Triplet Interaction Improves Graph Transformers: Accurate Molecular Graph Learning with Triplet Graph Transformers

Md Shamim Hussain, Mohammed Zaki, Dharmashankar Subramanian

ICML 2024arXiv:2402.04538
17
citations

What Improves the Generalization of Graph Transformers? A Theoretical Dive into the Self-attention and Positional Encoding

Hongkang Li, Meng Wang, Tengfei Ma et al.

ICML 2024arXiv:2406.01977
19
citations