Graph Data Augmentation

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9 papers2 papers in the last four weeks, with none the four weeks before. 0.0% of all new papers.

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

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Graph Data Augmentation via Contrastive Generator Inversion ( $\texttt{DCBA}$ )

Mateusz Stolarski, Michał Czuba, Łukasz Kraiński, Katarzyna Musial+3

Wrocław University of Science and Technology · University of Technology Sydney · SGH Warsaw School of Economics+1

Synthetic Data Augmentation Data Augmentation Graph Generation Graph Data Augmentation

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Graphs provide a natural representation of many complex systems, ranging from social platforms to ecosystems. However, the development of graph-based machine learning methods is often constrained by the limited availability of large and diverse graph datasets. In this paper, we introduce

\texttt{DCBA}

, a model-based approach to graph data augmentation that infers the configuration of a synthetic graph generator from an observed network. We instantiate the proposed framework using the

\texttt{ABCD}

generator, which produces scale-free networks with community structure. Our model learns a joint representation of graphs and generator parametrisations using a multi-positive contrastive objective with soft negative weighting. The learned representation enables the prediction of an

\texttt{ABCD}

configuration whose stochastic realisations preserve the macrostructural properties encoded by the generator. Experiments show that

\texttt{DCBA}

recovers generator parameters more accurately and robustly than an algorithmic inverse-modelling baseline. Its downstream utility is further demonstrated in community detection, where inferred configurations used to fine-tune

\texttt{PRoCD}

improve AMI on average by

161\%

on synthetic and

273\%

on real-world networks.