Class-Imbalanced Learning on Graphs (CILG)

This repository contains a curated list of papers focused on Class-Imbalanced Learning on Graphs (CILG). We have organized them into two primary groups: (1) data-level methods and (2) algorithm-level methods. Data-level methods are further subdivided into (i) data interpolation, (ii) adversarial generation, and (iii) pseudo-labeling. Algorithm-level methods are categorized into (i) model refinement, (ii) loss function engineering, and (iii) post-hoc adjustments.

We aim to keep this list up to date. If you come across any errors or papers that should be included, please feel free to open an issue or submit a pull request. We appreciate your contributions in maintaining the quality and relevance of this repository.

Survey Paper

Class-Imbalanced Learning on Graphs: A Survey.

Yihong Ma, Yijun Tian, Nuno Moniz, and Nitesh V. Chawla.

If you find this repository useful in your research, we would greatly appreciate it if you could cite our paper in your work. Thank you for your support!

@article{ma2023class,
  title={Class-Imbalanced Learning on Graphs: A Survey},
  author={Ma, Yihong and Tian, Yijun and Moniz, Nuno and Chawla, Nitesh V},
  journal={arXiv preprint arXiv:2304.04300},
  year={2023}
}

Data-Level Methods

Data Interpolation

• Graph Neural Network with Curriculum Learning for Imbalanced Node Classification, in Neurocomputing 2024. [pdf]

• Rethinking Semi-Supervised Imbalanced Node Classification from Bias-Variance Decomposition, in NeurIPS 2023. [pdf] [code]

• Semantic-aware Node Synthesis for Imbalanced Heterogeneous Information Networks, in CIKM 2023. [pdf] [code]

• HOVER: Homophilic Oversampling via Edge Removal for Class-Imbalanced Bot Detection on Graphs, in CIKM 2023. [pdf]

• GraphSHA: Synthesizing Harder Samples for Class-Imbalanced Node Classification, in KDD 2023. [pdf] [code]

• Imbalanced Node Classifcation Beyond Homophilic Assumption, in IJCAI 2023. [pdf]

• GraphMixup: Improving Class-Imbalanced Node Classification by Reinforcement Mixup and Self-supervised Context Prediction, in ECML/PKDD 2022. [pdf] [code]

• GATSMOTE: Improving Imbalanced Node Classification on Graphs via Attention and Homophily, in Mathematics 2022. [pdf]

• GraphENS: Neighbor-Aware Ego Network Synthesis for Class-Imbalanced Node Classification, in ICLR 2021. [pdf] [code]

• GraphSMOTE: Imbalanced Node Classification on Graphs with Graph Neural Networks, in WSDM 2021. [pdf] [code]

Adversarial Generation

• Anonymity Can Help Minority: A Novel Synthetic Data Over-sampling Strategy on Multi-label Graphs, in ECML/PKDD 2022. [pdf]

• ImGAGN: Imbalanced Network Embedding via Generative Adversarial Graph Networks, in KDD 2021. [pdf] [code]

Pseudo-Labeling

• GraphSR: A Data Augmentation Algorithm for Imbalanced Node Classification, in AAAI 2023. [pdf]

• Distance-wise Prototypical Graph Neural Network for Imbalanced Node Classification, in MLG 2022. [pdf] [code]

• Exploring Self-training for Imbalanced Node Classification, in ICONIP 2021. [pdf]

• SPARC: Self-Paced Network Representation for Few-Shot Rare Category Characterization, in KDD 2018. [pdf] [code]

Algorithm-Level Methods

Please note that certain papers may be relevant to more than one category.

Model Refinement

• QTIAH-GNN: Quantity and Topology Imbalance-aware Heterogeneous Graph Neural Network for Bankruptcy Prediction, in KDD 2023. [pdf]

• ImGCL: Revisiting Graph Contrastive Learning on Imbalanced Node Classification, in AAAI 2023. [pdf]

• Co-Modality Graph Contrastive Learning for Imbalanced Node Classification, in NeurIPS 2022. [pdf] [code]

• LTE4G: Long-Tail Experts for Graph Neural Networks, in CIKM 2022. [pdf] [code]

• A Kernel Propagation-Based Graph Convolutional Network Imbalanced Node Classification Model on Graph Data, in ICNSC 2022. [pdf]

• Effective-aggregation Graph Convolutional Network for Imbalanced Classification, in ICNSC 2022. [pdf]

• Attention and Cost-Sensitive Graph Neural Network for Imbalanced Node Classification, in ICNSC 2022. [pdf]

• Diving into Unified Data-Model Sparsity for Class-Imbalanced Graph Representation Learning, in GLFrontiers 2022. [pdf] [code]

• Network Embedding with Completely-imbalanced Labels, in TKDE 2020 . [pdf] [code]

• RSDNE: Exploring Relaxed Similarity and Dissimilarity from Completely-Imbalanced Labels for Network Embedding, in AAAI 2018. [pdf] [code]

• ImVerde: Vertex-Diminished Random Walk for Learning Imbalanced Network Representation, in Big Data 2018. [pdf] [code]

Loss Function Engineering

• QTIAH-GNN: Quantity and Topology Imbalance-aware Heterogeneous Graph Neural Network for Bankruptcy Prediction, in KDD 2023. [pdf]

• ImGCL: Revisiting Graph Contrastive Learning on Imbalanced Node Classification, in AAAI 2023. [pdf]

• TAM: Topology-Aware Margin Loss for Class-Imbalanced Node Classification, in ICML 2022. [pdf] [code]

• Co-Modality Graph Contrastive Learning for Imbalanced Node Classification, in NeurIPS 2022. [pdf] [code]

• FACS-GCN: Fairness-Aware Cost-Sensitive Boosting of Graph Convolutional Networks, in IJCNN 2022. [pdf] [code]

• Attention and Cost-Sensitive Graph Neural Network for Imbalanced Node Classification, in ICNSC 2022. [pdf]

• Diving into Unified Data-Model Sparsity for Class-Imbalanced Graph Representation Learning, in GLFrontiers 2022. [pdf] [code]

• Topology-Imbalance Learning for Semi-Supervised Node Classification, in NeurIPS 2021. [pdf] [code]

• FRAUDRE: Fraud Detection Dual-Resistant to Graph Inconsistency and Imbalance, in ICDM 2021. [pdf] [code]

Post-hoc Adjustments

• LTE4G: Long-Tail Experts for Graph Neural Networks, in CIKM 2022. [pdf] [code]

• Multi-Class Imbalanced Graph Convolutional Network Learning, in IJCAI 2020. [pdf] [code]

Acknowledgement

This page has been created and is maintained by Yihong Ma (yma5@nd.edu).