Graph condensation involves the process of simplifying complex graphs while preserving essential structural information. 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: Graph Condensation: A Survey. This survey paper serves as an excellent starting point for understanding the fundamentals of graph condensation and exploring its diverse applications.
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
- Graph Condensation: A Survey (Xinyi Gao et al., Arxiv 2024)
- A Comprehensive Survey on Graph Reduction: Sparsification, Coarsening, and Condensation (Mohammad Hashemi & Shengbo Gong et al., Arxiv 2024)
- A Survey on Graph Condensation (Hongjia Xu et al., Arxiv 2024)
- Graph Condensation for Graph Neural Networks (Wei Jin et al., ICLR 2022)
- Multiple sparse graphs condensation (Jian Gao et al., KBS 2023)
- Structure-free Graph Condensation: From Large-scale Graphs to Condensed Graph-free Data (Xin Zheng et al., NeurIPS 2023)
- Attend Who is Weak: Enhancing Graph Condensation via Cross-Free Adversarial Training (Xinglin Li et al., 2023)
- Two Trades is not Baffled: Condensing Graph via Crafting Rational Gradient Matching (Tianle Zhang & Yuchen Zhang & Kai Wang et al., 2024)
- Navigating Complexity: Toward Lossless Graph Condensation via Expanding Window Matching (Yuchen Zhang & Tianle Zhang & Kai Wang et al., 2024)
- Graph Data Condensation via Self-expressive Graph Structure Reconstruction (Zhanyu Liu & Chaolv Zeng et al., 2024)
- Condensing Graphs via One-Step Gradient Matching (Wei Jin et al., KDD 2022)
- Graph Condensation via Receptive Field Distribution Matching (Mengyang Liu et al., 2022)
- Kernel Ridge Regression-Based Graph Dataset Distillation (Zhe Xu et al., KDD 2023)
- Fast Graph Condensation with Structure-based Neural Tangent Kernel (Lin Wang et al., 2023)
- Disentangled Condensation for Large-scale Graphs (Zhenbang Xiao et al.,2024 )
- EXGC: Bridging Efficiency and Explainability in Graph Condensation (Junfeng Fang et al., WWW 2024)
- Simple Graph Condensation (Zhenbang Xiao et al., 2024)
- Does Graph Distillation See Like Vision Dataset Counterpart? (Beining Yang & Kai Wang et al., NeurIPS 2023)
- Graph Condensation via Eigenbasis Matching (Yang Liu et al., 2023)
- Mirage: Model-Agnostic Graph Distillation for Graph Classification (Mridul Gupta et al., 2023)
- Fair Graph Distillation (Qizhang Feng et al., NeurIPS 2023)
- GCARe: Mitigating Subgroup Unfairness in Graph Condensation through Adversarial Regularization (Runze Mao et al., Applied Sciences 2023)
- CaT: Balanced Continual Graph Learning with Graph Condensation (Yilun Liu et al., ICDM 2023)
- PUMA: Efficient Continual Graph Learning with Graph Condensation (Yilun Liu et al., 2023)
- Faster Hyperparameter Search for GNNs via Calibrated Dataset Condensation (Mucong Ding et al., 2023)
- FedGKD: Unleashing the Power of Collaboration in Federated Graph Neural Networks (Qiying Pan et al., 2023)
- Graph Condensation for Inductive Node Representation Learning (Xinyi Gao et al., 2023)
- Heterogeneous Graph Condensation (Jian Gao et al., TKDE 2024)
In addition to this Graph Condensation Papers Repository, you may find the following related repositories valuable for your research and exploration:
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.
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.