Graph Transformer

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10 papers in the last four weeks, against 1 the four weeks before. 0.1% of all new papers.

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Latest papers 63

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Geometry-Aware Superpixel Graph Transformer with Metadata for Skin Lesion Classification
Jun 18, 2026Muhammad Azeem, Tanveer Hussain, Amr Ahmed +1Skin Lesion Classification Graph Representation Learning
Edge Hill University, Ormskirk, Lancashire, L39 4QP, United Kingdom
Graph Grounded Cross Attention Transformer Neural Network for Structurally Constrained Full Event Sequence Generation in Predictive Process Monitoring
Jun 17, 2026Fang Wang, Ernesto DamianiPredictive Process Monitoring Process Mining
Department of Computer Science, University of Milan, Via Giovanni Celoria, 18, Milano 20133, MI, Italy
scGTN: Deep Siamese Graph Transformer Network for Single-cell RNA Sequencing Clustering
Jun 17, 2026Jinke Wu, Yifan Wang, Siyu Yi +5Graph SSL Clustering
Sichuan University · University of International Business and Economics · Great Bay University +1
SGFormer++: Semantic Graph Transformer for Incremental 3D Scene Graph Generation
Jun 13, 2026Mengshi Qi, Changsheng Lv, Zijian Fu +2Class-Incremental Learning 3D SGG
State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, China
Controlled Dynamics Attractor Transformer
Jun 13, 2026Cheng Zhang, Minnan Luo, Zesheng Yang +3Energy-Based Models Graph Classification
School of Computer Science and Technology, MOEK-LINNS Lab, Xi’an Jiaotong University, Xi’an, Shaanxi, China · Department of Computer Science and Technology, Tsinghua University, Beijing, China
Probabilistic Contrastive Pretraining for Multi-task ADME Property Prediction
Jun 9, 2026Yifan Xue, Srimukh Prasad Veccham, Saee Paliwal +2Contrastive Learning Self-Supervised Pre-Training
NVIDIA Santa Clara, CA 95051
Spatiotemporal Graph Transformer for 3D Neighborhood Interaction and Quality Prediction in Metal Additive Manufacturing
Jun 8, 2026Joyce Karen Pelaez, Siqi Zhang, Hoo Sang KoAdditive Manufacturing Multimodal Graph Learning
Department of Industrial Engineering, Southern Illinois University Edwardsville, Box 1805, Edwardsville, 62026, IL, USA.
Graph Set Transformer
Jun 3, 2026Jose E. Escrig Molina, Baoquan Chen, Daniel ProbstGraph Neural Networks Graph Representation Learning
Bioinformatics Group Wageningen University Droevendaalsesteeg 6708 PB Wageningen The Netherlands · Department of Physics Technical University of Munich 85748 Garching Germany
In-Context Graphical Inference
Jun 3, 2026Zehua Cheng, Wei Dai, Jiahao SunAmortized Inference Probabilistic Graphical Models
Department of Computer Science, University of Oxford, Oxford, United Kingdom · FLock.io, London, United Kingdom
Multi-Modal Graph Neural Network with Transformer-Guided Adaptive Diffusion for Preclinical Alzheimer Classification
Jun 2, 2026Jaeyoon Sim, Minjae Lee, Guorong Wu +1Graph Neural Networks Alzheimer's Disease Classification
Pohang University of Science and Technology, Pohang, South Korea · University of North Carolina at Chapel Hill, Chapel Hill, USA
RelGT-AC: A Relational Graph Transformer for Autocomplete Tasks in Relational Databases
Jun 2, 2026Phillip JiangRelational Deep Learning Graph Transformer
Appsofa LLC
On Efficient Scaling of GNNs via IO-Aware Layers Implementations
May 29, 2026Daria Fomina, Daniil Krasylnikov, Alexey Boykov +3Graph Attention Networks GPU Kernel Optimization
1Yandex · 2HSE University · 3ITMO University
Lost in Tokenization: Fundamental Trade-offs in Graph Tokenization for Transformers
May 21, 2026Maya Bechler-Speicher, Gilad Yehudai, Gil Harari +3Transformer Expressivity Graph Representation Learning
Meta AI · John A. Paulson School of Engineering and Applied Sciences, Harvard University · Google Research +2
GHI: Graphormer over Conditioned Hypergraph Incidence for Aspect-Based Sentiment Analysis
May 21, 2026Yu Du, Wenlong Zhu, Xingze Li +3Hypergraph Neural Networks Aspect-Based Sentiment Analysis
Qiqihar University
Velocityformer: Broken-Symmetry-Matched Equivariant Graph Transformers for Cosmological Velocity Reconstruction
May 20, 2026Tilman Tröster, David Mirkovic, Veronika Oehl +1Equivariant Neural Networks Equivariant GNNs
Department of Physics ETH Zürich
GraViti: Graph-Level Variational Autoencoders with Relaxed Permutation Invariance
May 15, 2026Roman Bresson, Konstantinos Divriotis, Johannes F. Lutzeyer +2Variational Autoencoders Molecular Graph Generation
Mohamed bin Zayed University of Artificial Intelligence · LIX, CNRS, École Polytechnique, IP Paris
Hypergraph Pattern Machine: Compositional Tokenization for Higher-Order Interactions
May 15, 2026Kyrie Zhao, Zehong Wang, Tianyi Ma +5Feature Interaction Modeling Hypergraph Neural Networks
University of Notre Dame · Stanford University · Yale University +2
Gaussian Relational Graph Transformer
May 15, 2026Zezhong Ding, Jin Li, Xugang Wang +1Graph Attention Networks Graph Representation Learning
School of Artificial Intelligence and Data Science, University of Science and Technology of China (USTC) · Data Darkness Lab, Suzhou Institute for Advanced Research, USTC · School of Biomedical Engineering, USTC +1
Teaching LLMs to See Graphs: Unifying Text and Structural Reasoning
May 11, 2026Dario VajdaLLM Reasoning with Graphs Text-Attributed Graph Learning
Faculty of Computer and Information Science University of Ljubljana
Heterogeneous Dependency Graph-Guided Attentionfor Patent Representation Learning
May 11, 2026Yongmin Yoo, Qiongkai Xu, Zhangkai Wu +1Representation Learning Legal IR
Frontier AI Research Centre, Macquarie University
CTQWformer: A CTQW-based Transformer for Graph Classification
May 10, 2026Zhan Li, Wuqing Yu, Yusen Wu +1Graph Representation Learning Quantum-Inspired ML
school of Artificial Intelligence, Beijing Normal University, Beijing, China
Attention-based graph neural networks: a survey
May 9, 2026Chengcheng Sun, Chenhao Li, Xiang Lin +4Graph Attention Networks Graph Neural Networks
School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, Jiangsu, 221116, China. · Mine Digitization Engineering Research Center, Ministry of Education of the People’s Republic of China, Xuzhou, Jiangsu, 221116, China.
Soft Graph Diffusion Transformer for MIMO Detection
May 1, 2026Nan Jiang, Jiadong Hong, Lei Liu +3Diffusion Transformer Wireless Communications
College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou, China · Theory Lab, Central Research Institute, 2012 Labs, Huawei Technologies Co., Ltd., Hong Kong, China
STLGT: A Scalable Trace-Based Linear Graph Transformer for Tail Latency Prediction in Microservices
Apr 29, 2026Yongliang Ding, Qigong Bi, Peng PuEfficient Neural Network Inference Microservice Architecture
East China Normal University Shanghai, China
Distance-Misaligned Training in Graph Transformers and Adaptive Graph-Aware Control
Apr 24, 2026Qinhan Hou, Jing TangGraph Attention Networks Stochastic Block Model
University of Helsinki Helsinki, Finland
Dual-stream Spatio-Temporal GCN-Transformer Network for 3D Human Pose Estimation
Apr 20, 2026Jiawen Duan, Jian Xiang, Zhiqiang Li +23D ViTs 3D Reconstruction
School of Information and Electronic Engineering, Zhejiang University of Science and Technology, Liuxia, Hangzhou 310023, China
Capacity-Controlled Global Attention for Graph Transformers
Apr 19, 2026Yang Liu, Dongxin Guo, Tom Zheng +3Gated Attention Transformer Attention
Brain Investing Limited Hong Kong, China · The University of Hong Kong Hong Kong, China · Stellaris AI Limited Hong Kong, China
Scalable and Adaptive Parallel Training of Graph Transformer on Large Graphs
Apr 17, 2026Jun-Liang Lin, Kamesh Madduri, Mahmut Taylan KandemirDistributed Training Graph Transformer
The Pennsylvania State University University Park, PA, USA
DyGnROLE: Asymmetric Pretraining for Edge Classification on Dynamic Graphs
Feb 26, 2026Tyler Bonnet, Marek ReiGraph Representation Learning Self-Supervised Pre-Training
Imperial College London
Context-aware Skin Cancer Epithelial Cell Classification with Scalable Graph Transformers
Feb 17, 2026Lucas Sancéré, Noémie Moreau, Katarzyna BozekComputational Pathology Oncology
Faculty of Mathematics and Natural Sciences, University of Cologne, Cologne, North Rhine-Westphalia, Germany · Institute for Biomedical Informatics, Faculty of Medicine and University Hospital Cologne, University of Cologne, North Rhine-Westphalia, Germany · Center for Molecular Medicine Cologne (CMMC), Faculty of Medicine and University Hospital Cologne, University of Cologne, North Rhine-Westphalia, Germany +1

Jun 18, 2026, cs.CVJ/K move · Enter open · S save

Geometry-Aware Superpixel Graph Transformer with Metadata for Skin Lesion Classification

Muhammad Azeem, Tanveer Hussain, Amr Ahmed, Ardhendu Behera

Edge Hill University, Ormskirk, Lancashire, L39 4QP, United Kingdom

Skin Lesion Classification Graph Representation Learning Multimodal Classification Medical Image Classification Graph Transformer

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Automated skin cancer classification from dermoscopic images remains challenging due to heterogeneous lesion structure, strong intra-class variability, and subtle visual differences between benign and malignant cases. Existing CNN/ViT pipelines typically rely on global or patch-level features and often combine patient metadata via late fusion, which limits spatially grounded multimodal reasoning. We present a novel region-based graph learning framework that explicitly models lesions as graphs of spatially coherent superpixel regions represented as frozen CNN features. To capture fine-grained lesion arrangements, we encode inter-regional geometry as edge attributes and introduce a dedicated metadata context node connected to all regions, providing structured integration of demographic/clinical variables within the same relational space. Node representations are updated using our edge-aware graph transformer followed by attention-driven propagation, and a final graph-level embedding for benign-malignant classification. Experiments on four public benchmarks demonstrate that explicit region-level relational modeling and graph-native multimodal fusion yield consistent gains over the state-of-the-art. Consequently, we establish a new graph-centric perspective in which CNN features are modeled as relational nodes and improved through contextual integration, yielding more expressive and robust classifications.

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Latest papers 63