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https://github.com/yanliang3612/awesome-imbalanced-learning-on-graphs
A repository contains a collection of resources and papers on Imbalance Learning On Graphs
https://github.com/yanliang3612/awesome-imbalanced-learning-on-graphs
List: awesome-imbalanced-learning-on-graphs
graph-neural-networks imbalanced-learning robust-machine-learning
Last synced: 3 months ago
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A repository contains a collection of resources and papers on Imbalance Learning On Graphs
- Host: GitHub
- URL: https://github.com/yanliang3612/awesome-imbalanced-learning-on-graphs
- Owner: yanliang3612
- Created: 2023-03-15T13:00:33.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-04-05T04:19:31.000Z (7 months ago)
- Last Synced: 2024-05-21T03:12:35.853Z (6 months ago)
- Topics: graph-neural-networks, imbalanced-learning, robust-machine-learning
- Homepage:
- Size: 3.3 MB
- Stars: 72
- Watchers: 2
- Forks: 8
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Awesome Imbalanced Learning on Graphs
[]([https://github.com/ZhiningLiu1998/awesome-imbalanced-learning](https://github.com/yanliang3612/awesome-imbalanced-learning-on-graphs))
[](https://opensource.org/licenses/MIT)
This repository contains a collection of resources and papers on ***Imbalance Learning On Graphs***😆. We are pleased to see that more and more efforts have been devoted to dealing with imbalanced graph-structured data since 2020, which is more complicated than images and text.
If you have any relevant paper or codes to update the list, please pull a request or report an issue.
(The image is generated by [DALL-E-3](https://openai.com/dall-e-3), thanks, [DALLE](https://openai.com/dall-e-3)!!!🎉)
# Contents
- [Survery](#survery)
- [Quantity Imbalance](#quantity-imbalance)
- [Data-Augmentation](#data-augmentation)
- [Over-Sampling](#over-sampling)
- [Loss function Engineering](#loss-function-engineering)
- [Knowledge Distillation Technique](#knowledge-distillation-technique)
- [Self-Supervised Learning](#self-supervised-learning)
- [Reinforcement Learning](#reinforcement-learning)
- [Community Bias](#community-bias)
- [Degree Imbalance](#degree-imbalance)
- [Semi-Supervised Learning](#semi-supervised-learning)
- [Unsupervised Learning](#unsupervised-learning)
- [Topology Imbalance](#topology-imbalance)
- [Graph Classification](#graph-classification)
- [Multi-modal Learning](#multi-modal-learning)
# Papers
## Survery
**A Survey of Graph Neural Networks in Real world: Imbalance, Noise, Privacy and OOD Challenges**
> *Wei Ju, Siyu Yi, Yifan Wang, Zhiping Xiao, Zhengyang Mao, Hourun Li, Yiyang Gu, Yifang Qin, Nan Yin, Senzhang Wang, Xinwang Liu, Xiao Luo, Philip S. Yu, Ming Zhang* \
> Arxiv 2024. [[paper](https://arxiv.org/abs/2403.04468)]\
> 7 Mar 2024
**A survey of imbalanced learning on graphs: Problems, techniques, and future directions**
> *A Survey of Imbalanced Learning on Graphs: Problems, Techniques, and Future Directions* \
> Arxiv 2023. [[paper](https://arxiv.org/abs/2308.13821)]\
> 26 Aug 2023
**Class-imbalanced learning on graphs: A survey**
> *Yihong Ma, Yijun Tian, Nuno Moniz, Nitesh V. Chawla* \
> Arxiv 2023. [[paper](https://arxiv.org/abs/2304.04300)]\
> 9 Apr 2023
---
## Quantity Imbalance
### Data Augmentation
**Topological Augmentation for Class-Imbalanced Node Classification**
> *Zhining Liu, Zhichen Zeng, Ruizhong Qiu, Hyunsik Yoo, David Zhou, Zhe Xu, Yada Zhu, Kommy Weldemariam, Jingrui He, Hanghang Tong* \
> Arxiv 2023. [[paper](https://arxiv.org/abs/2308.14181)]\
> 27 Aug 2023
**Rethinking Semi-Supervised Imbalanced Node Classification from Bias-Variance Decomposition**
> *Divin Yan, Gengchen Wei, Chen Yang, Shengzhong Zhang, Zengfeng Huang* \
> NeurIPS 2023. [[paper](https://arxiv.org/abs/2310.18765)] [[code](https://github.com/yanliang3612/ReVar)]\
> 21 Sep 2023
### Over-Sampling
**Semantic-aware Node Synthesis for Imbalanced Heterogeneous Information Networks**
> *Xinyi Gao, Wentao Zhang, Tong Chen, Junliang Yu, Hung Quoc Viet Nguyen, Hongzhi Yin* \
> CIKM 2023. [[paper](https://dl.acm.org/doi/abs/10.1145/3583780.3615055)]\
> 27 Feb 2023
**INS-GNN: Improving graph imbalance learning with self-supervision**
> *Xin Juan, Fengfeng Zhou, Wentao Wang, Wei Jin, Jiliang Tang, Xin Wang* \
> Information Sciences. [[paper](https://www.sciencedirect.com/science/article/abs/pii/S0020025523005042)]\
> 30 Aug 2023
**GraphSHA: Synthesizing Harder Samples for Class-Imbalanced Node Classification**
> *Wen-Zhi Li, Chang-Dong Wang, Hui Xiong, Jian-Huang Lai* \
> KDD 2023. [[paper](https://arxiv.org/abs/2306.09612)]\
> 16 Jun 2023
**GraphSR: A Data Augmentation Algorithm for Imbalanced Node Classification**
> *Mengting Zhou, Zhiguo Gong* \
> AAAI 2023. [[paper](https://arxiv.org/abs/2302.12814)]\
> 24 Feb 2023
**UNREAL: Unlabled Nodes Retrieval and Labeling for Heavily-imbalanced Node Classification**
> *Divin Yan, Shengzhong Zhang, Bisheng Li, Min Zhou, Zengfeng Huang* \
> Openreview 2022. [[paper](https://arxiv.org/abs/2303.10371)]\
> 30 Sep 2022
**GraphENS: Neighbor-aware ego network synthesis for class-imbalanced node classification**
> *Joonhyung Park1, Jaeyun Song1, Eunho Yang* \
> ICLR 2022. [[paper](https://openreview.net/forum?id=MXEl7i-iru)] [[code](https://github.com/JoonHyung-Park/GraphENS)]\
> 18 Aug 2022
**GraphMixup: Improving Class-Imbalanced Node Classification on Graphs by Self-supervised Context Prediction**
> *Lirong Wu, Haitao Lin, Zhangyang Gao, Cheng Tan, Stan.Z.Li* \
> ECML-PKDD 2022. [[Paper](https://arxiv.org/abs/2106.11133)] [[code](https://github.com/LirongWu/GraphMixup)] \
> 21 Jun 2021
**ImGAGN:Imbalanced Network Embedding via Generative Adversarial Graph Networks**
> *Liang Qu, Huaisheng Zhu, Ruiqi Zheng, Yuhui Shi, Hongzhi Yin* \
> KDD 2021. [[paper](https://arxiv.org/abs/2106.02817)] [[code](https://github.com/Leo-Q-316/ImGAGN)]\
> 05 Jun 2021
**GraphSMOTE: Imbalanced Node Classification on Graphs with Graph Neural Networks**
> *Tianxiang Zhao, Xiang Zhang, Suhang Wang,* \
> WSDM 2021. [[paper](https://arxiv.org/abs/2103.08826)] [[code](https://github.com/TianxiangZhao/GraphSmote)]\
> 16 Mar 2021
**Multi-Class Imbalanced Graph Convolutional Network Learning**
> *Min Shi, Yufei Tang, Xingquan Zhu, David Wilson, Jianxun Liu* \
> IJCAI 2020. [[paper](https://arxiv.org/abs/2210.05274)] [[code](https://github.com/codeshareabc/DRGCN)] \
> 07 Jan 2021
### Loss Function Engineering
**Hyperbolic Geometric Graph Representation Learning for Hierarchy-imbalance Node Classification**
> *Xingcheng Fu, Yuecen Wei, Qingyun Sun, Haonan Yuan, Jia Wu, Hao Peng, Jianxin Li* \
> WWW 2023. [[paper](https://arxiv.org/abs/2304.05059)] \
> 11 Apr 2023
**Minority-Weighted Graph Neural Network for Imbalanced Node Classification in Social Networks of Internet of People**
> *Kefan Wang, Jing An, Mengchu Zhou, Zhe Shi, Xudong Shi, Qi Kang* \
> IEEE Internet of Things Journal. [[paper](https://ieeexplore.ieee.org/abstract/document/9875203)] \
> 01 Jun 2022
**TAM: Topology-Aware Margin Loss for Class-Imbalanced Node Classification**
> *Jaeyun Song1, Joonhyung Park1, Eunho Yang* \
> ICML 2022. [[paper](https://arxiv.org/abs/2206.01729)] [[code](https://github.com/Jaeyun-Song/TAM)] \
> 01 Jun 2022
**BA-GNN: On Learning Bias-Aware Graph Neural Network**
> *Zhengyu Chen, Teng Xiao, Kun Kuang* \
> ICDE 2022. [[paper](https://ieeexplore.ieee.org/document/9835653)] \
> 09 May 2022
**FRAUDRE: Fraud Detection Dual-Resistant to Graph Inconsistency and Imbalance**
> *Ge Zhang, Jia Wu, Jian Yang, Amin Beheshti, Shan Xue, Chuan Zhou, Quan Z. Sheng* \
> ICDM 2021. [[paper](https://ieeexplore.ieee.org/document/9679178)] [[github](https://github.com/FraudDetection/FRAUDRE)] \
> 07 Dec 2021
### Knowledge Distillation Technique
**LTE4G: Long-Tail Experts for Graph Neural Networks**
> *Sukwon Yun, Kibum Kim, Kanghoon Yoon, Chanyoung Park* \
> CIKM 2021. [[paper](https://arxiv.org/abs/2208.10205)] [[github](https://github.com/SukwonYun/LTE4G)] \
> 19 Apr 2022
### Self-Supervised Learning
**Co-Modality Graph Contrastive Learning for Imbalanced Node Classification**
> *Yiyue Qian, Chunhui Zhang, Yiming Zhang, Qianlong Wen, Yanfang Ye, Chuxu Zhang* \
> NeurIPS 2022. [[paper](https://openreview.net/forum?id=f_kvHrM4Q0)] [[github](https://github.com/graphprojects/CM-GCL)] \
> 01 Nov 2022
**Distance-wise Prototypical Graph Neural Network in Node Imbalance Classification**
> *Yu Wang, Charu Aggarwal, Tyler Derr* \
> arXiv 2021. [[paper](https://arxiv.org/abs/2110.12035)] [[github](https://github.com/YuWVandy/DPGNN)] \
> 19 Apr 2022
### Reinforcement Learning
**AUC-oriented Graph Neural Network for Fraud Detection**
> *Mengda Huang , Yang Liu , Xiang Ao , Kuan Li , Jianfeng Chi , Jinghua Feng , Hao Yang , Qing He* \
> WWW 2022. [[paper](https://dl.acm.org/doi/10.1145/3485447.3512178)] \
> 25 Apr 2022
---
## Community Bias
**Understanding Community Bias Amplification in Graph Representation Learning**
> *Shengzhong Zhang, Wenjie Yang, Yimin Zhang, Hongwei Zhang, Divin Yan, Zengfeng Huang* \
> Arxiv 2023. [[paper](https://arxiv.org/abs/2312.04883)]\
> 8 Dec 2023
---
## Degree Imbalance
### Semi-Supervised Learning
**Toward Degree Bias in Embedding-Based Knowledge Graph Completion**
> *Harry Shomer, Wei Jin, Wentao Wang, Jiliang Tang* \
> WWW 2023. [[paper](https://arxiv.org/abs/2302.05044)]\
> 10 Feb 2023
**ResNorm: Tackling Long-tailed Degree Distribution Issue in Graph Neural Networks via Normalization**
> *Langzhang Liang, Zenglin Xu, Zixing Song, Irwin King, Jieping Ye* \
> arXiv 2022. [[paper](https://arxiv.org/abs/2206.08181)]\
> 16 Jun 2022
**BA-GNN: On Learning Bias-Aware Graph Neural Network**
> *Zhengyu Chen, Teng Xiao, Kun Kuang* \
> ICDE 2022. [[paper](https://ieeexplore.ieee.org/document/9835653)] \
> 09 May 2022
**LTE4G: Long-Tail Experts for Graph Neural Networks**
> *Sukwon Yun, Kibum Kim, Kanghoon Yoon, Chanyoung Park* \
> CIKM 2021. [[paper](https://arxiv.org/abs/2208.10205)] [[code](https://github.com/SukwonYun/LTE4G)] \
> 19 Apr 2022
**RawlsGCN: Towards Rawlsian Difference Principle on Graph Convolutional Network**
> *Jian Kang, Yan Zhu, Yinglong Xia, Jiebo Luo, Hanghang Tong* \
> WWW 2022. [[paper](https://arxiv.org/abs/2202.13547)] [[code](https://github.com/jiank2/RawlsGCN)]\
> 28 Feb 2022
**Bilateral Filtering Graph Convolutional Network for Multi-relational Social Recommendation in the Power-law Networks**
> *Minghao Zhao, Qilin Deng, Kai Wang, Runze Wu, Jianrong Tao, Changjie Fan, Liang Chen, Peng Cui* \
> TOIS 2021. [[paper](https://dl.acm.org/doi/10.1145/3469799)]\
> 21 Sep 2021
**Tail-GNN: Tail-Node Graph Neural Networks**
> *Zemin Liu, Trung-Kien Nguyen, Yuan Fang* \
> KDD 2021. [[paper](https://dl.acm.org/doi/10.1145/3447548.3467276)] [[code](https://github.com/shuaiOKshuai/Tail-GNN)]\
> 14 Aug 2021
**Towards locality-aware meta-learning of tail node embeddings on networks**
> *Zemin Liu, Wentao Zhang, Yuan Fang, Xinming Zhang, Steven C.H. Hoi* \
> CIKM 2020. [[paper](https://dl.acm.org/doi/10.1145/3340531.3411910)] [[code](https://github.com/smufang/meta-tail2vec)] \
> 19 Oct 2020
**Investigating and Mitigating Degree-Related Biases in Graph Convolutional Networks**
> *Xianfeng Tang, Huaxiu Yao, Yiwei Sun, Yiqi Wang, Jiliang Tang, Charu Aggarwal, Prasenjit Mitra, Suhang Wang* \
> CIKM 2020. [[paper](https://arxiv.org/abs/2006.15643)]\
> 28 Jun 2020
### Unsupervised Learning
**Uncovering the Structural Fairness in Graph Contrastive Learning**
> *Ruijia Wang, Xiao Wang, Chuan Shi, Le Song* \
> NeurIPS 2022. [[paper](https://arxiv.org/abs/2210.03011)] [[code](https://github.com/BUPT-GAMMA/Uncovering-the-Structural-Fairness-in-Graph-Contrastive-Learning)]\
> 06 Oct 2022
---
## Topology Imbalance
**Topology Uncertainty Modeling For Imbalanced Node Classification on Graphs**
> *Jiayi Gao, Jiaxing Li, Ke Zhang, Youyong Kong*\
> ICASSP 2023. [[paper](https://ieeexplore.ieee.org/abstract/document/10094813)]\
> 05 May 2023
**Position-aware Structure Learning for Graph Topology-imbalance by Relieving Under-reaching and Over-squashing**
> *Qingyun Sun, Jianxin Li, Haonan Yuan, Xingcheng Fu, Hao Peng, Cheng Ji, Qian Li, Philip S. Yu*\
> CIKM 2022. [[paper](https://arxiv.org/abs/2208.08302)]\
> 17 Aug 2022
**TopoImb: Toward Topology-level Imbalance in Learning from Graphs**
> *Tianxiang Zhao, Dongsheng Luo, Xiang Zhang, Suhang Wang*\
> LOG 2022. [[paper](https://openreview.net/forum?id=nR3rZ4ODtQ)]\
> 23 Nov 2022
**Position-aware Structure Learning for Graph Topology-imbalance by Relieving Under-reaching and Over-squashing**
> *Qingyun Sun, Jianxin Li, Haonan Yuan, Xingcheng Fu, Hao Peng, Cheng Ji, Qian Li, Philip S. Yu* \
> CIKM 2022. [[paper](https://arxiv.org/abs/2208.08302)] [[code](https://github.com/RingBDStack/PASTEL)] \
> 17 Aug 2022
**Topology-Imbalance Learning for Semi-Supervised Node Classification**
> *Deli Chen, Yankai Lin, Guangxiang Zhao, Xuancheng Ren, Peng Li, Jie Zhou, Xu Sun*\
> NeurIPS 2021. [[paper](https://arxiv.org/abs/2110.04099)] [[code](https://github.com/victorchen96/ReNode)] \
> 08 Oct 2021
---
## Graph Classification
**Imbalanced Graph Classification via Graph-of-Graph Neural Networks**
> *Yu Wang, Yuying Zhao, Neil Shah, Tyler Derr* \
> CIKM 2022. [[paper](https://dl.acm.org/doi/abs/10.1145/3511808.3557356)] [[code](https://github.com/YuWVandy/G2GNN)]\
> 17 Oct 2022
**On Size-Oriented Long-Tailed Graph Classification of Graph Neural Networks**
> *Zemin Liu, Qiheng Mao, Chenghao Liu, Yuan Fang, Jianling Sun* \
> WWW 2022. [[paper](https://dl.acm.org/doi/10.1145/3485447.3512197)] [[code](https://github.com/shuaiOKshuai/SOLT-GNN)]\
> 25 Apr 2022
**RAHNet: Retrieval Augmented Hybrid Network for Long-tailed Graph Classification**
> *Zhengyang Mao, Wei Ju, Yifang Qin, Xiao Luo, Ming Zhang* \
> ACM MM 2023. [[paper](https://dl.acm.org/doi/abs/10.1145/3581783.3612360)]\
> 4 Aug 2023
---
## Multi-modal Learning
**Co-Modality Graph Contrastive Learning for Imbalanced Node Classification**
> *Yiyue Qian, Chunhui Zhang, Yiming Zhang, Qianlong Wen, Yanfang Ye, Chuxu Zhang* \
> NeurIPS 2022. [[paper](https://openreview.net/forum?id=f_kvHrM4Q0)] [[code](https://github.com/graphprojects/CM-GCL)]\
> 15 Sept 2022
---
# Contact
- [Divin Yan ([email protected])](https://divinyan.com).