Graph Condensation Papers

Graph condensation (GC) is a data-centric approach that accelerates GNN model training by creating a compact yet representative graph to replace the original graph. It enables GNNs trained on the condensed graph to match the performance of those trained on the original graph.

This repository aims to provide a comprehensive resource for researchers and practitioners interested in exploring various aspects of graph condensation.

For a detailed overview of graph condensation techniques and their applications, we recommend reading our survey paper on TKDE'25: 🔥Graph Condensation: A Survey and our tutorial at WWW'25: Graph Condensation: Foundations, Methods and Prospects. The survey paper serves as an excellent starting point for understanding the fundamentals of graph condensation and exploring its diverse applications.

To understand the underlying mechanism of optimization strategies in graph condensation, we highly recommend the paper 🔥🔥🔥Rethinking and Accelerating Graph Condensation: A Training-Free Approach with Class Partition as essential reading.

Note: recommended papers are marked by 📌.

Latest Updates

[28/06/2025] Dynamic Graph Condensation (Dong Chen et al. ArXiv'25)
[28/06/2025] Simple yet Effective Graph Distillation via Clustering (Yurui Lai et al. KDD'25)
[28/06/2025] GDCK: Efficient Large-Scale Graph Distillation Utilizing a Model-Free Kernelized Approach (Yue Zhang et al. PAKDD'25)
[28/06/2025] Adapting Precomputed Features for Efficient Graph Condensation (Yuan Li et al. ICML'25)
[11/05/2025] ST-GCond: Self-supervised and Transferable Graph Dataset Condensation (Beining Yang et al. ICLR'25)
[11/05/2025] Bonsai: Gradient-free Graph Condensation for Node Classification (Mridul Gupta et al. ICLR'25)
[11/05/2025] Bi-Directional Multi-Scale Graph Dataset Condensation via Information Bottleneck (Xingcheng Fu et al. AAAI'25)
[11/05/2025] Structure Balance and Gradient Matching-Based Signed Graph Condensation (Rong Li et al. AAAI'25)
[11/05/2025] Rethinking Federated Graph Learning: A Data Condensation Perspective (Hao Zhang et al. Arxiv'25)
[11/05/2025] FedC4: Graph Condensation Meets Client-Client Collaboration for Efficient and Private Federated Graph Learning (Zekai Chen et al. Arxiv'25)

View More Updates

[02/03/2025] Scalable Graph Condensation with Evolving Capabilities (Shengbo Gong et al. Arxiv'25)
[02/02/2025] Exploring Hypergraph Condensation via Variational Hyperedge Generation and Multi-Aspectual Amelioration (Zheng Gong et al. WWW'25)
[01/02/2025] Random Walk Guided Hyperbolic Graph Distillation (Yunbo Long et al. Arxiv'25)
[09/01/2025] Efficient Graph Condensation via Gaussian Process (Lin Wang et al. Arxiv'25)
[09/01/2025] GraphDART: Graph Distillation for Efficient Advanced Persistent Threat Detection (Saba Fathi Rabooki et al. Arxiv'25)
[09/01/2025] Training-free Heterogeneous Graph Condensation via Data Selection (Yuxuan Liang et al. ICDE'25)
[27/11/2024] Contrastive Graph Condensation: Advancing Data Versatility through Self-Supervised Learning (Xinyi Gao et al. Arxiv'24) [05/09/2024] GSTAM: Efficient Graph Distillation with Structural Attention-Matching (Arash Rasti-Meymandi et al. ECCV'24)
[28/08/2024] Self-Supervised Learning for Graph Dataset Condensation (Yuxiang Wang et al. KDD'24)
[31/07/2024] Backdoor Graph Condensation (Jiahao Wu et al. Arxiv'24)
[20/07/2024] TinyGraph: Joint Feature and Node Condensation for Graph Neural Networks (Yezi Liu et al. Arxiv'24)

Contribution

We welcome contributions to enhance the breadth and depth of this repository. If you have a paper related to graph condensation that you believe should be included, please feel free to submit a pull request. Together, we can build a valuable resource for the graph condensation community.

| conference/journal'year | [paper_name](paper_link) | Authors | [[code]](code_link) |

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Contents

The repository is organized into categories to facilitate easy navigation and exploration of papers related to graph condensation, including effectiveness, efficiency, generalization, fairness and applications.

• Graph Condensation Papers
  • Latest Updates
  • Contribution
  • Contents
  • Survey
  • Tutorial
  • Methodology
    • Effective Graph Condensation
    • Efficient Graph Condensation
    • Generalized Graph Condensation
    • Fair Graph Condensation
    • Robust Graph Condensation
  • Applications
    • Graph Continual Learning
    • Hyper-Parameter/Neural Architecture Search
    • Federated Learning
    • Inference Acceleration
    • Heterogeneous Graph
    • Hypergraph Graph
    • Signed Graph
    • Dynamic Graph
    • Security
  • Open-Source Libraries
  • Related Repositories
  • Contact

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Survey

TKDE'25	📌Graph Condensation: A Survey	Xinyi Gao et al.
IJCAI'24	A Comprehensive Survey on Graph Reduction: Sparsification, Coarsening, and Condensation	Mohammad Hashemi & Wei Jin et al.
TKDD'25	Learning to Reduce the Scale of Large Graphs: A Comprehensive Survey	Hongjia Xu et al.

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Tutorial

WWW'25	📌Graph Condensation: Foundations, Methods and Prospects	Hongzhi Yin, Xinyi Gao et al.	[Website]

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Methodology

Effective Graph Condensation

ICLR'22	GCond	📌Graph Condensation for Graph Neural Networks	Wei Jin et al.	[code]
KBS'23	MSGC	Multiple Sparse Graphs Condensation	Jian Gao et al.
NeurIPS'23	SFGC	📌Structure-free Graph Condensation: From Large-scale Graphs to Condensed Graph-free Data	Xin Zheng et al.	[code]
Arxiv'23	GroC	Attend Who is Weak: Enhancing Graph Condensation via Cross-Free Adversarial Training	Xinglin Li et al.
Arxiv'24	CTRL	Two Trades is not Baffled: Condensing Graph via Crafting Rational Gradient Matching	Tianle Zhang et al.	[code]
ICML'24	GEOM	📌Navigating Complexity: Toward Lossless Graph Condensation via Expanding Window Matching	Yuchen Zhang et al.	[code]
KDD'24	GCSR	Graph Data Condensation via Self-expressive Graph Structure Reconstruction	Zhanyu Liu et al.	[code]
KDD'24	SGDC	📌Self-Supervised Learning for Graph Dataset Condensation	Yuxiang Wang et al.	[code]
ECCV'24	GSTAM	GSTAM: Efficient Graph Distillation with Structural Attention-Matching	Arash Rasti-Meymandi et al.	[code]
Arxiv'25	HyDRO	Random Walk Guided Hyperbolic Graph Distillation	Yunbo Long et al.
AAAI'25	BiMSGC	Bi-Directional Multi-Scale Graph Dataset Condensation via Information Bottleneck	Xingcheng Fu et al.
WWW'25	TinyGraph	Beyond Node Condensation: Learning Tiny Graphs via Joint Graph Condensation	Yezi Liu et al.

Efficient Graph Condensation

KDD'22	DosCond	Condensing Graphs via One-Step Gradient Matching	Wei Jin et al.	[code]
Arxiv'22	GCDM	📌Graph Condensation via Receptive Field Distribution Matching	Mengyang Liu et al.
KDD'23	KIDD	Kernel Ridge Regression-Based Graph Dataset Distillation	Zhe Xu et al.	[code]
WWW'24	GC-SNTK	Fast Graph Condensation with Structure-based Neural Tangent Kernel	Lin Wang et al.
ICLR'24	Mirage	Mirage: Model-Agnostic Graph Distillation for Graph Classification	Mridul Gupta et al.	[code]
WWW'25	DisCo	Disentangled Condensation for Large-scale Graphs	Zhenbang Xiao et al.	[code]
WWW'24	EXGC	EXGC: Bridging Efficiency and Explainability in Graph Condensation	Junfeng Fang et al.	[code]
PKDD'24	SimGC	Simple Graph Condensation	Zhenbang Xiao et al.	[code]
WWW'25	CGC	📌Rethinking and Accelerating Graph Condensation: A Training-Free Approach with Class Partition	Xinyi Gao et al.	[code]
Arxiv'25	GCGP	Efficient Graph Condensation via Gaussian Process	Lin Wang et al.	[code]
Arxiv'25	GECC	Scalable Graph Condensation with Evolving Capabilities	Shengbo Gong et al.
ICLR'25	Bonsai	Bonsai: Gradient-free Graph Condensation for Node Classification	Mridul Gupta et al.	[code]
ICML'25	GCPA	Adapting Precomputed Features for Efficient Graph Condensation	Yuan Li et al.	[code]
KDD'25	ClustGDD	Simple yet Effective Graph Distillation via Clustering	Yurui Lai et al.	[code]
PAKDD '25	GDCK	GDCK: Efficient Large-Scale Graph Distillation Utilizing a Model-Free Kernelized Approach	Yue Zhang et al.	[code]

Generalized Graph Condensation

NeurIPS'23	SGDD	Does Graph Distillation See Like Vision Dataset Counterpart?	Beining Yang et al.	[code]
ICML'24	GDEM	📌Graph Distillation with Eigenbasis Matching	Yang Liu et al.	[code]
KDD'24	OpenGC	Graph Condensation for Open-World Graph Learning	Xinyi Gao et al.
KDD'25	CTGC	Contrastive Graph Condensation: Advancing Data Versatility through Self-Supervised Learning	Xinyi Gao et al.
ICLR'25	ST-GCond	📌ST-GCond: Self-supervised and Transferable Graph Dataset Condensation	Beining Yang et al.	[code]

Fair Graph Condensation

NeurIPS'23	FGD	📌Fair Graph Distillation	Qizhang Feng et al.
AS'23	GCARe	GCARe: Mitigating Subgroup Unfairness in Graph Condensation through Adversarial Regularization	Runze Mao et al.

Robust Graph Condensation

TKDE'25	RobGC	RobGC: Towards Robust Graph Condensation	Xinyi Gao et al.	[code]

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Applications

Graph Continual Learning

ICDM'23	CaT	CaT: Balanced Continual Graph Learning with Graph Condensation	Yilun Liu et al.	[code]
TKDE'24	PUMA	📌PUMA: Efficient Continual Graph Learning with Graph Condensation	Yilun Liu et al.	[code]

Hyper-Parameter/Neural Architecture Search

Arxiv'23	HCDC	Faster Hyperparameter Search for GNNs via Calibrated Dataset Condensation	Mucong Ding et al.

Federated Learning

NeurIPS'23	FedGKD	📌FedGKD: Unleashing the Power of Collaboration in Federated Graph Neural Networks	Qiying Pan et al.
AAAI'25	FedGC	Federated Graph Condensation with Information Bottleneck Principles	Bo Yan
Arxiv'25	FedC4	FedC4: Graph Condensation Meets Client-Client Collaboration for Efficient and Private Federated Graph Learning	Zekai Chen
Arxiv'25	FedGM	Rethinking Federated Graph Learning: A Data Condensation Perspective	Hao Zhang

Inference Acceleration

ICDE'24	MCond	Graph Condensation for Inductive Node Representation Learning	Xinyi Gao et al.

Heterogeneous Graph

TKDE'24	HGCond	Heterogeneous Graph Condensation	Jian Gao et al.	[code]
ICDE'25	FreeHGC	📌Training-free Heterogeneous Graph Condensation via Data Selection	Yuxuan Liang et al.	[code]

Hypergraph Graph

WWW'25	HG-Cond	Exploring Hypergraph Condensation via Variational Hyperedge Generation and Multi-Aspectual Amelioration	Zheng Gong et al.

Signed Graph

AAAI'25	SGSGC	Structure Balance and Gradient Matching-Based Signed Graph Condensation	Rong Li et al.

Dynamic Graph

ArXiv'25	DyGC	Dynamic Graph Condensation	Dong Chen et al.

Security

ICDE'25	BGC	📌Backdoor Graph Condensation	Jiahao Wu et al.	[code]
Arxiv'25	GraphDART	GraphDART: Graph Distillation for Efficient Advanced Persistent Threat Detection	Saba Fathi Rabooki et al.

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Open-Source Libraries

Library	Paper	Implementation	#GC Methods	#Datasets	Tasks
GCondenser	[paper]	PyG, DGL	6	7	Node classification
GC-Bench	[paper]	PyG	9	12	Node classification, graph classification, link prediction, node clustering, anomaly detection
GraphSlim	[paper]	PyG	7	5	Node classification

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Related Repositories

In addition to this Graph Condensation Papers Repository, you may find the following related repositories valuable for your research and exploration:

• Awesome-graph-reduction
• Awesome-Graph-Scaling
• Awesome-Dataset-Distillation

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Contact

For any inquiries or suggestions regarding this repository, please don't hesitate to contact us by opening an issue on this repository.

Thank you for your interest in the Graph Condensation Papers Repository. We hope you find it valuable for your research and exploration. If you find this repository to be useful, please cite our survey paper.

@article{gao2025graph,
  title={Graph condensation: A survey},
  author={Gao, Xinyi and Yu, Junliang and Chen, Tong and Ye, Guanhua and Zhang, Wentao and Yin, Hongzhi},
  journal={IEEE Transactions on Knowledge and Data Engineering},
  year={2025},
  publisher={IEEE}
}