Graph Data Augmentation

Latest papers

12 papers3 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

RGDM: Robust Graph Diffusion Model
Nov 3, 2026Yancheng Wang, Ping Li, Dongfang Sun +1Graph Diffusion Graph Robustness
School of Computing and Augmented Intelligence Arizona State University Tempe, AZ 85281, USA · VecML Inc. Bellevue, WA 98004, USA
SAC-GCL: Structure-Aware Compression for Graph Contrastive Learning
Oct 27, 2026Muhammad Ifte khairul Islam, Tanvir Hossain, Esra AkbasGraph Contrastive Learning Graph Data Augmentation
Department of Computer Science, Texas A&M University- Texarkana, Texarkana,Texas, USA · Department Of Computer Science, Georgia State University, Atlanta, GA, USA · Department Of Computer Science, Georgia State University, Atlanta, Georgia, USA
MCDA: Multi-View Graph Contrastive Learning with Dynamic Topology Augmentation for Functional MRI Analysis
Oct 19, 2026Hao Zhang, Xiaoyun Liu, Shuo Huang +4Graph Contrastive Learning Functional Magnetic Resonance Imaging
College of Biomedical Engineering, Fudan University, Shanghai 200433, China · College of Information Science and Technology & Artificial Intelligence, Nanjing Forestry University, Nanjing 210037, China · Department of Psychosomatics and Psychiatry, Zhongda Hospital, School of Medicine, Jiangsu Provincial Key Laboratory of Brain Science and Medicine, Southeast University, Nanjing 210009, China +3
Graph Data Augmentation via Contrastive Generator Inversion ( $\texttt{DCBA}$ )
Oct 5, 2026Mateusz Stolarski, Michał Czuba, Łukasz Kraiński +4Synthetic Data Augmentation Data Augmentation
Wrocław University of Science and Technology · University of Technology Sydney · SGH Warsaw School of Economics +1
Learning the Latent Structure: A Feature-Centric Approach to Graph Data Augmentation
Oct 1, 2026Yu Song, Zhigang Hua, Yan Xie +5Graph Data Augmentation Data Augmentation
Michigan State University · Meta
GATTA: Graph Active Learning with Test-Time Augmentation
Aug 15, 2026Zsombor Bánfi, András Gézsi, András FormanekGraph Data Augmentation Active Learning
Department of Artificial Intelligence and Systems Engineering, Budapest University of Technology and Economics, Magyar tudósok krt. 2, Budapest, H-1117, Hungary. · Department of Electrical Engineering (ESAT), STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, KU Leuven, 3001 Leuven, Belgium.
RADE: Random Add-Drop Edge as a Regularizer
May 30, 2026Danial Saber, Amirali Salehi-AbariNeural Network Generalization Graph Neural Networks
Ontario Tech University, Oshawa, Ontario, Canada.
Instance Discrimination for Link Prediction
May 18, 2026Valentin Cuzin-Rambaud, Mathieu Lefort, Rémy CazabetData Augmentation Link Prediction
SyCoSMA, DM2L, LIRIS, UCBL · Université Lyon 1, INSA Lyon, CNRS, LIRIS, UMR 5205, Lyon, France. · LIRIS, SyCoSMA, IRISA, MALT, UR +2
Disentangle-then-Refine: LLM-Guided Decoupling and Structure-Aware Refinement for Graph Contrastive Learning
Apr 16, 2026Zhaoxing Li, Hai-Feng Zhang, Xiaoming ZhangDisentangled Representation Learning Data Augmentation
Institute of Physical Science and Information Technology, Anhui University · School of Mathematical Sciences, Anhui University · Qinghai Institute of Science and Technology Information, Xining, China
Adversarial Label Invariant Graph Data Augmentations for Out-of-Distribution Generalization
Apr 9, 2026Simon Zhang, Ryan P. DeMilt, Kun Jin +1OOD Generalization Covariate Shift
Department of Computer Science, Purdue University, West Lafayette, USA · Department of Computer Science and Engineering, The Ohio State University, Columbus Ohio, USA · Department of Industrial and Systems Engineering, The Ohio State University, Columbus Ohio, USA
RABot: Reinforcement-Guided Graph Augmentation for Imbalanced and Noisy Social Bot Detection
Feb 25, 2026Longlong Zhang, Xi Wang, Haotong Du +3Social Bot Detection Graph Data Augmentation
School of Artificial Intelligence, Optics and Electronics (iOPEN), Northwestern Polytechnical University · Shenzhen Research Institute, Northwestern Polytechnical University · School of Automation Science and Engineering, Xi’an Jiaotong University +1
GraphIFE: Rethinking Graph Imbalance Node Classification via Invariant Learning
Sep 28, 2025Fanlong Zeng, Wensheng Gan, Kangjie Chen +1Class-Imbalanced Learning Graph Neural Networks
School of Intelligent Systems Science and Engineering, Jinan University, Zhuhai 519070, China · School of Cybersecurity, Tianjin University, Tianjin 300354, China · University of Illinois Chicago, Chicago, USA

Nov 3, 2026J/K move · Enter open · S save

RGDM: Robust Graph Diffusion Model

Yancheng Wang, Ping Li, Dongfang Sun, Yingzhen Yang

School of Computing and Augmented Intelligence Arizona State University Tempe, AZ 85281, USA · VecML Inc. Bellevue, WA 98004, USA

Graph Diffusion Graph Robustness Graph Data Augmentation Node Classification

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Diffusion models represent a powerful class of generative models known for their solid theoretical foundations and remarkable performance across diverse tasks and domains. Existing node level diffusion methods synthesize nodes and edges within a graph, but are not designed to learn a robust generator from adversarially corrupted graphs. In this study, we introduce the Robust Graph Diffusion Model (RGDM), designed to generate labeled synthetic graph structures consisting of nodes and edges that integrate seamlessly into a given graph. The RGDM consists of a Robust Graph Autoencoder (RGAE) and a Latent Diffusion Model (LDM). Leveraging an edge selection mechanism and an innovative low-rank regularization on the latent feature, the RGDM produces clean and high-quality synthetic graph structures, even when trained on graphs subject to adversarial attacks. Comprehensive experimental evaluations reveal that Graph Neural Networks (GNNs) trained on the augmented graph, which is formed by merging the original attacked graph with the synthetic graph structures, exhibit significantly improved robustness against various graph adversarial attacks in the context of semi-supervised node classification. The code of the RGDM is available at https://github.com/Statistical-Deep-Learning/RGDM.