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Awesome literature on imbalanced learning on graphs
https://github.com/Xtra-Computing/Awesome-Literature-ILoGs
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Awesome literature on imbalanced learning on graphs
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URL: https://github.com/Xtra-Computing/Awesome-Literature-ILoGs
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ultimate-awesome - Awesome-Literature-ILoGs - Awesome literature on imbalanced learning on graphs. (Other Lists / Monkey C Lists)
README

        # Awesome Literature on [Imbalanced Learning on Graphs](https://arxiv.org/abs/2308.13821) (ILoGs)

This repository showcases a curated collection of research literature on imbalanced learning on graphs. We have categorized this literature according to the taxonomies of **Problems** and **Techniques** detailed in our survey paper, titled [A Survey of Imbalanced Learning on Graphs: Problems, Techniques, and Future Directions](https://arxiv.org/abs/2308.13821). In this repository, we primarily arrange the literature based on our **Problem** taxonomy for clarity. For a deeper understanding of this rapidly evolving and challenging field, we encourage readers to consult our survey.

For our taxonomy of **Problems**, we classify the literature based on **class imbalance** and **structure imbalance**, both stemming from imbalanced input. We further distill this into more specific categories: node-, edge-, and graph-level imbalance, offering a comprehensive understanding of graph imbalance.

For an overview of imbalanced learning on various data, please refer to Github Repository [Awesome-Imbalanced-Learning](https://github.com/ZhiningLiu1998/awesome-imbalanced-learning).

# Our Survey Paper

[A Survey of Imbalanced Learning on Graphs: Problems, Techniques, and Future Directions](https://arxiv.org/abs/2308.13821)   

Zemin Liu, Yuan Li, Nan Chen, Qian Wang, Bryan Hooi, Bingsheng He.

We provide the BibTeX formatted citation for our survey paper as follows. If you find our work helpful, we greatly appreciate your citation of our paper.

      @article{liu2023surveyilogs,

        title={A Survey of Imbalanced Learning on Graphs: Problems, Techniques, and Future Directions},

        author={Liu, Zemin and Li, Yuan and Chen, Nan and Wang, Qian and Hooi, Bryan and He, Bingsheng},

        journal={arXiv preprint arXiv:2308.13821},

        year={2023}

      }

      

# Outline

The outline corresponds to the taxonomy of Problems in our [survey paper](https://arxiv.org/abs/2308.13821).

- [1. Class Imbalance](https://github.com/Xtra-Computing/Awesome-Literature-ILoGs#1-class-imbalance)

  - [1.1 Node-Level Class Imbalance](https://github.com/Xtra-Computing/Awesome-Literature-ILoGs#11-node-level-class-imbalance)

    - [1.1.1 Imbalanced Node CLassification](https://github.com/Xtra-Computing/Awesome-Literature-ILoGs#111-imbalanced-node-classification)

    - [1.1.2 Node-Level Anomaly Detection](https://github.com/Xtra-Computing/Awesome-Literature-ILoGs#112-node-level-anomaly-detection)

    - [1.1.3 Few-Shot Node Classification](https://github.com/Xtra-Computing/Awesome-Literature-ILoGs#113-few-shot-node-classification)

    - [1.1.4 Zero-Shot Node Classification](https://github.com/Xtra-Computing/Awesome-Literature-ILoGs#114-zero-shot-node-classification)

  - [1.2 Edge-Level Class Imbalance](https://github.com/Xtra-Computing/Awesome-Literature-ILoGs#12-edge-level-class-imbalance)

    - [1.2.1 Few-Shot Link Prediction](https://github.com/Xtra-Computing/Awesome-Literature-ILoGs#121-few-shot-link-prediction)

    - [1.2.2 Edge-Level Anomaly Detection](https://github.com/Xtra-Computing/Awesome-Literature-ILoGs#122-edge-level-anomaly-detection)

    - [1.2.3 Imbalanced Link Classification](https://github.com/Xtra-Computing/Awesome-Literature-ILoGs#123-imbalanced-link-classification)

  - [1.3 Graph-Level Class Imbalance](https://github.com/Xtra-Computing/Awesome-Literature-ILoGs#13-graph-level-class-imbalance)

    - [1.3.1 Imbalanced Graph Classification](https://github.com/Xtra-Computing/Awesome-Literature-ILoGs#131-imbalanced-graph-classification)

    - [1.3.2 Graph-Level Anomaly Detection](https://github.com/Xtra-Computing/Awesome-Literature-ILoGs#132-graph-level-anomaly-detection)

    - [1.3.3 Few-Shot Graph Classification](https://github.com/Xtra-Computing/Awesome-Literature-ILoGs#133-few-shot-graph-classification)

- [2. Structure Imbalance](https://github.com/Xtra-Computing/Awesome-Literature-ILoGs#2-structure-imbalance)

  - [2.1 Node-Level Structure Imbalance](https://github.com/Xtra-Computing/Awesome-Literature-ILoGs#21-node-level-structure-imbalance)

    - [2.1.1 Imbalanced Node Degrees](https://github.com/Xtra-Computing/Awesome-Literature-ILoGs#211-imbalanced-node-degrees)

    - [2.1.2 Node Topology Imbalance](https://github.com/Xtra-Computing/Awesome-Literature-ILoGs#212-node-topology-imbalance)

    - [2.1.3 Long-Tail Entity Embedding on KGs](https://github.com/Xtra-Computing/Awesome-Literature-ILoGs#213-long-tail-entity-embedding-on-kgs)

  - [2.2 Edge-Level Structure Imbalance](https://github.com/Xtra-Computing/Awesome-Literature-ILoGs#22-edge-level-structure-imbalance)

    - [2.2.1 Few-Shot Relation Classification](https://github.com/Xtra-Computing/Awesome-Literature-ILoGs#221-few-shot-relation-classification)

    - [2.2.2 Zero-Shot Relation Classification](https://github.com/Xtra-Computing/Awesome-Literature-ILoGs#222-zero-shot-relation-classification)

    - [2.2.3 Few-Shot Reasoning on KGs](https://github.com/Xtra-Computing/Awesome-Literature-ILoGs#223-few-shot-reasoning-on-kgs)

  - [2.3 Graph-Level Structure Imbalance](https://github.com/Xtra-Computing/Awesome-Literature-ILoGs#23-graph-level-structure-imbalance)

    - [2.3.1 Imbalanced Graph Sizes](https://github.com/Xtra-Computing/Awesome-Literature-ILoGs#231-imbalanced-graph-sizes)

    - [2.3.2 Imbalanced Topology Groups](https://github.com/Xtra-Computing/Awesome-Literature-ILoGs#232-imbalanced-topology-groups)

- [3. Other Related Literature](https://github.com/Xtra-Computing/Awesome-Literature-ILoGs#3-other-related-literature)

  - [3.1 Fairness Learning on Graphs](https://github.com/Xtra-Computing/Awesome-Literature-ILoGs#31-fairness-learning-on-graphs)

# Literature

## 1. Class Imbalance

### 1.1 Node-Level Class Imbalance

#### 1.1.1 Imbalanced Node CLassification

| Name  | Title | Venue | Paper | Code |

| ------------- | ------------- | ------------- | ------------- | ------------- |

| DR-GCN  | Multi-Class Imbalanced Graph Convolutional Network Learning | IJCAI 2020  |  [PDF](https://www.ijcai.org/proceedings/2020/398)  | [TensorFlow](https://github.com/codeshareabc/DRGCN)  |

| DPGNN  | Distance-wise Prototypical Graph Neural Network for Imbalanced Node Classification  | arXiv 2021  | [PDF](https://arxiv.org/abs/2110.12035)  | [PyTorch](https://github.com/YuWVandy/DPGNN)  |

| GraphSMOTE  | GraphSMOTE: Imbalanced Node Classification on Graphs with Graph Neural Networks  | WSDM 2021  | [PDF](https://arxiv.org/abs/2103.08826)  | [PyTorch](https://github.com/TianxiangZhao/GraphSmote)  |

| ImGAGN  | ImGAGN: Imbalanced Network Embedding via Generative Adversarial Graph Networks  | KDD 2021  | [PDF](https://arxiv.org/abs/2106.02817)  | [PyTorch](https://github.com/Leo-Q-316/ImGAGN)  |

| TAM  | TAM: Topology-Aware Margin Loss for Class-Imbalanced Node Classification  | ICML 2022  | [PDF](https://proceedings.mlr.press/v162/song22a/song22a.pdf)  | [PyTorch](https://github.com/Jaeyun-Song/TAM)  |

| LTE4G  | LTE4G: Long-Tail Experts for Graph Neural Networks  | CIKM 2022  | [PDF](https://arxiv.org/abs/2208.10205)  | [PyTorch](https://github.com/SukwonYun/LTE4G)  |

| GraphMixup  | GraphMixup: Improving Class-Imbalanced Node Classification on Graphs by Self-supervised Context Prediction  | ECML-PKDD 2022  | [PDF](https://arxiv.org/abs/2106.11133)  | [PyTorch](https://github.com/LirongWu/GraphMixup)  |

| GraphENS  | GraphENS: Neighbor-Aware Ego Network Synthesis for Class-Imbalanced Node Classification   | ICLR 2022  | [PDF](https://openreview.net/forum?id=MXEl7i-iru)  | [PyTorch](https://github.com/JoonHyung-Park/GraphENS)  |

| ALLIE  | ALLIE: Active Learning on Large-scale Imbalanced Graphs  | WWW 2022  | [PDF](https://dl.acm.org/doi/10.1145/3485447.3512229)  | [N/A]  |

| GraphSANN  | Imbalanced Node Classification Beyond Homophilic Assumption  | IJCAI 2023  | [PDF](https://arxiv.org/abs/2304.14635)  | 	[N/A]  |

|GraphSR|GraphSR: a data augmentation algorithm for imbalanced node classification|AAAI 2023|[PDF](https://ojs.aaai.org/index.php/AAAI/article/view/25622)|[PyTorch](https://github.com/CastalZhou/graphsr)|

|ImGCL|ImGCL: Revisiting Graph Contrastive Learning on Imbalanced Node Classification|AAAI 2023|[PDF](https://ojs.aaai.org/index.php/AAAI/article/view/26319)|[N/A]|CIKM 2023

|SNS|Semantic-aware Node Synthesis for Imbalanced Heterogeneous Information Networks|CIKM 2023|[PDF](https://arxiv.org/pdf/2302.14061)|[PyTorch](https://github.com/XYGaoG/SNS)|

|GraphSANN|Imbalanced Node Classification Beyond Homophilic Assumption|IJCAI 2023|[PDF](https://arxiv.org/pdf/2304.14635)|[N/A]|

|ReVar|Rethinking Semi-Supervised Imbalanced Node Classification from Bias-Variance Decomposition|NeurIPS 2023|[PDF](https://proceedings.neurips.cc/paper_files/paper/2023/file/5d1233f819202ade06023346df80a6d2-Paper-Conference.pdf)|[PyTorch](https://github.com/yanliang3612/ReVar)|

|BNE|Balanced neighbor exploration for semi-supervised node classification on imbalanced graph data|Information Sciences 2023|[PDF](https://www.sciencedirect.com/science/article/pii/S0020025523002529)|[N/A]|

|INS-GNN|INS-GNN: Improving graph imbalance learning with self-supervision|Information Sciences 2023|[PDF](https://www.sciencedirect.com/science/article/pii/S0020025523005042)|[N/A]|

|GraphSHA|GraphSHA: Synthesizing Harder Samples for Class-Imbalanced Node Classification|KDD 2023|[PDF](https://arxiv.org/pdf/2306.09612)|[PyTorch](https://github.com/wenzhilics/GraphSHA)|

|GNN-CL|Graph Neural Network with Curriculum Learning for Imbalanced Node Classification|Neurocomputing 2024|[PDF](https://arxiv.org/pdf/2202.02529)|[N/A]|

|Graph-DAO|A novel graph oversampling framework for node classification in class-imbalanced graphs|SCIS 2024|[PDF](http://scis.scichina.com/en/2024/162101.pdf)|[N/A]|

#### 1.1.2 Node-Level Anomaly Detection

| Name  | Title | Venue | Paper | Code |

| ------------- | ------------- | ------------- | ------------- | ------------- |

| Amplay  | Node re-ordering as a means of anomaly detection in time-evolving graphs  | ECML PKDD 2016  | [PDF](https://link.springer.com/chapter/10.1007/978-3-319-46227-1_11)  | [N/A]  |

|   | An embedding approach to anomaly detection  | ICDE 2016  | [PDF](https://ieeexplore.ieee.org/abstract/document/7498256/)  | [C++](https://github.com/hurenjun/EmbeddingAnomalyDetection)  |

| PFrauDetector  | PFrauDetector: A Parallelized Graph Mining Approach for Efficient Fraudulent Phone Call Detection  | ICPADS 2016  | [PDF](https://ieeexplore.ieee.org/abstract/document/7823855/)  | [N/A]  |

| HitFraud  | HitFraud: A Broad Learning Approach for Collective Fraud Detection in Heterogeneous Information Networks  | ICDM 2017  | [PDF](https://ieeexplore.ieee.org/abstract/document/8215553/)  | [N/A]  |

| FRAUDAR  | Graph-based fraud detection in the face of camouflage  | TKDD 2017  | [PDF](https://dl.acm.org/doi/abs/10.1145/3056563)  | [NumPy](https://bhooi.github.io/projects/fraudar/index.html)  |

| HiDDen  | Hidden: hierarchical dense subgraph detection with application to financial fraud detection  | SDM 2017  | [PDF](https://epubs.siam.org/doi/abs/10.1137/1.9781611974973.64)  | [MATLAB](https://github.com/sizhang92/HiDDen-SDM17)  |

| ALAD  | Accelerated Local Anomaly Detection via Resolving Attributed Networks  | IJCAI 2017  | [PDF](https://www.ijcai.org/Proceedings/2017/0325.pdf)  | [NumPy](https://github.com/ninghaohello/ALAD)  |

| GANG  | GANG: Detecting fraudulent users in online social networks via guilt-by-association on directed graphs  | ICDM 2017  | [PDF](https://home.engineering.iastate.edu/~neilgong/papers/GANG.pdf)  | [NumPy (Non-official)](https://github.com/safe-graph/UGFraud/blob/master/UGFraud/Detector/GANG.py)  |

| MTHL  | Anomaly detection in dynamic networks using multi-view time-series hypersphere learning  | CIKM 2017  | [PDF](https://xit22penny.github.io/files/pdf/research/2017-anomaly-detection.pdf)  | [NumPy](https://github.com/picsolab/Anomaly_Detection_MTHL)  |

|   | Spectrum-based deep neural networks for fraud detection  | CIKM 2017  | [PDF](https://arxiv.org/pdf/1706.00891.pdf)  | [N/A]  |

| Radar  | Radar: Residual Analysis for Anomaly Detection in Attributed Networks  | IJCAI 2017  | [PDF](https://www.ijcai.org/proceedings/2017/0299.pdf)  | [PyTorch](https://github.com/pygod-team/pygod)  |

| HoloScope  | HoloScope: Topology-and-Spike Aware Fraud Detection  | CIKM 2017  | [PDF](https://shenghua-liu.github.io/papers/cikm2017-holoscope.pdf)  | [NumPy](https://github.com/shenghua-liu/HoloScope)  |

| HoloScope  | A contrast metric for fraud detection in rich graphs  | TKDE 2018  | [PDF](https://ieeexplore.ieee.org/ielaam/69/8893432/8494803-aam.pdf)  | [NumPy](https://github.com/shenghua-liu/HoloScope)  |

| SEANO  | Semi-supervised embedding in attributed networks with outliers  | SDM 2018  | [PDF](https://arxiv.org/abs/1703.08100)  | [TensorFlow](http://jiongqianliang.com/SEANO/)  |

| Metagraph2vec  | Gotcha-sly malware! scorpion a metagraph2vec based malware detection system  | KDD 2018  | [PDF](https://dl.acm.org/doi/abs/10.1145/3219819.3219862)  | [N/A]  |

| PAICAN  | Bayesian robust attributed graph clustering: Joint learning of partial anomalies and group structure  | AAAI 2018  | [PDF](https://cdn.aaai.org/ojs/11642/11642-13-15170-1-2-20201228.pdf)  | [TensorFlow](https://github.com/abojchevski/paican)  |

| Netwalk  | NetWalk: A Flexible Deep Embedding Approach for Anomaly Detection in Dynamic Networks  | KDD 2018  | [PDF](https://www.researchgate.net/profile/Wei-Cheng-4/publication/329087157_A_Deep_Neural_Network_for_Unsupervised_Anomaly_Detection_and_Diagnosis_in_Multivariate_Time_Series_Data/links/5c7d7e04458515831f83ce81/A-Deep-Neural-Network-for-Unsupervised-Anomaly-Detection-and-Diagnosis-in-Multivariate-Time-Series-Data.pdf)  | [TensorFlow](https://github.com/chengw07/NetWalk)  |

| DeepSphere  | Deep into Hypersphere: Robust and Unsupervised Anomaly Discovery in Dynamic Networks  | IJCAI 2018  | [PDF](https://www.ijcai.org/Proceedings/2018/0378.pdf)  | [TensorFlow](https://github.com/picsolab/DeepSphere)  |

| NHAD  | NHAD: Neuro-Fuzzy Based Horizontal Anomaly Detection In Online Social Networks  | TKDE 2018  | [PDF](https://arxiv.org/abs/1804.06733)  | [N/A]  |

|   | Designing Size Consistent Statistics for Accurate Anomaly Detection in Dynamic Networks  | TKDD 2018  | [PDF](https://dl.acm.org/doi/abs/10.1145/3185059)  | [N/A]  |

| SPARC  | SPARC: Self-Paced Network Representation for Few-Shot Rare Category Characterization  | KDD 2018  | [PDF](https://dl.acm.org/doi/abs/10.1145/3219819.3219968)  | [Theano](https://sites.google.com/view/dawei-zhou/publications)  |

| ANOMALOUS  | ANOMALOUS: A Joint Modeling Approach for Anomaly Detection on Attributed Networks  | IJCAI 2018  | [PDF](https://www.ijcai.org/Proceedings/2018/0488.pdf)  | [MATLAB](https://github.com/zpeng27/ANOMALOUS)  |

| FFD  | Mining fraudsters and fraudulent strategies in large-scale mobile social networks  | TKDE 2019  | [PDF](https://ericdongyx.github.io/papers/TKDE19-yang-fraud-detection.pdf)  | [N/A]  |

| GraphUCB  | Interactive anomaly detection on attributed networks  | WSDM 2019  | [PDF](https://dl.acm.org/doi/abs/10.1145/3289600.3290964)  | [NumPy](https://github.com/kaize0409/GraphUCB_AnomalyDetection)  |

| Dominant  | Deep anomaly detection on attributed networks  | SDM 2019  | [PDF](https://www.researchgate.net/profile/Kaize-Ding/publication/332888297_Deep_Anomaly_Detection_on_Attributed_Networks/links/606f78364585150fe993abb6/Deep-Anomaly-Detection-on-Attributed-Networks.pdf)  | [PyTorch](https://github.com/kaize0409/GCN_AnomalyDetection_pytorch)  |

| AnomRank  | Fast and accurate anomaly detection in dynamic graphs with a two-pronged approach  | KDD 2019  | [PDF](https://dl.acm.org/doi/abs/10.1145/3292500.3330946)  | [C++](https://github.com/minjiyoon/KDD19-AnomRank)  |

| ONE  | Outlier aware network embedding for attributed networks  | AAAI 2019  | [PDF](https://aaai.org/ojs/index.php/AAAI/article/download/3763/3641)  | [NetworkX](https://github.com/sambaranban/ONE)  |

| SpecAE  | SpecAE: Spectral AutoEncoder for Anomaly Detection in Attributed Networks  | CIKM 2019  | [PDF](https://dl.acm.org/doi/abs/10.1145/3357384.3358074)  | [N/A]  |

| QANet  | QANet: Tensor Decomposition Approach for Query-Based Anomaly Detection in Heterogeneous Information Networks  | TKDE 2019  | [PDF](https://ieeexplore.ieee.org/abstract/document/8488508/)  | [N/A]  |

| MADAN  | Multi-scale anomaly detection on attributed networks  | AAAI 2020  | [PDF](https://cdn.aaai.org/ojs/5409/5409-13-8634-1-10-20200511.pdf)  | [NetworkX](https://github.com/leoguti85/MADAN)  |

| ALARM  | A Deep Multi-View Framework for Anomaly Detection on Attributed Networks  | TKDE 2020  | [PDF](https://ieeexplore.ieee.org/abstract/document/9162509/)  | [PyTorch (Non-official)](https://github.com/Kaslanarian/SAGOD)  |

| MIDAS  | Midas: Microcluster-based detector of anomalies in edge streams  | AAAI 2020  | [PDF](https://cdn.aaai.org/ojs/5724/5724-13-8949-1-10-20200513.pdf)  | [C++](https://github.com/Stream-AD/MIDAS)  |

| GraphRfi  | Gcn-based user representation learning for unifying robust recommendation and fraudster detection  | SIGIR 2020  | [PDF](https://www.researchgate.net/profile/Tong-Chen-64/publication/341539062_GCN-Based_User_Representation_Learning_for_Unifying_Robust_Recommendation_and_Fraudster_Detection/links/6100eb6c169a1a0103bf9a9b/GCN-Based-User-Representation-Learning-for-Unifying-Robust-Recommendation-and-Fraudster-Detection.pdf)  | [PyTorch](https://github.com/zsjdddhr/GraphRfi)  |

| BEA  | Anomaly Detection on Dynamic Bipartite Graph with Burstiness  | ICDM 2020  | [PDF](https://www.researchgate.net/profile/Aixin-Sun/publication/349201957_Anomaly_Detection_on_Dynamic_Bipartite_Graph_with_Burstiness/links/6143f0c0a609b152aa157610/Anomaly-Detection-on-Dynamic-Bipartite-Graph-with-Burstiness.pdf)  | [N/A]  |

| MixedAD  | Mixedad: a scalable algorithm for detecting mixed anomalies in attributed graphs  | AAAI 2020  | [PDF](https://cdn.aaai.org/ojs/5482/5482-13-8707-1-10-20200511.pdf)  | [N/A]  |

| STAGN  | Graph neural network for fraud detection via spatial-temporal attention  | TKDE 2020  | [PDF](https://ieeexplore.ieee.org/abstract/document/9204584/)  | [PyTorch](https://github.com/finint/antifraud)  |

| GAL  | Error-bounded graph anomaly loss for GNNs  | CIKM 2020  | [PDF](https://www.researchgate.net/profile/Tong-Zhao-20/publication/346275948_Error-Bounded_Graph_Anomaly_Loss_for_GNNs/links/5fc92515299bf188d4f13924/Error-Bounded-Graph-Anomaly-Loss-for-GNNs.pdf)  | [PyTorch](https://github.com/zhao-tong/Graph-Anomaly-Loss)  |

| GAAN  | Generative Adversarial Attributed Network Anomaly Detection  | CIKM 2020  | [PDF](https://static.aminer.cn/storage/pdf/acm/20/cikm/10.1145/3340531.3412070.pdf)  | [PyTorch](https://github.com/Kaslanarian/SAGOD)  |

| DMGD  | Integrating Network Embedding and Community Outlier Detection via Multiclass Graph Description  | ECAI 2020  | [PDF](https://ecai2020.eu/papers/1161_paper.pdf)  | [TensorFlow](https://github.com/vasco95/DMGD)  |

| CARE-GNN  | Enhancing Graph Neural Network-based Fraud Detectors against Camouflaged Fraudsters  | CIKM 2020  | [PDF](https://penghao-bdsc.github.io/papers/cikm20.pdf)  | [PyTorch](https://github.com/YingtongDou/CARE-GNN)  |

| GraphConsis  | Alleviating the Inconsistency Problem of Applying Graph Neural Network to Fraud Detection  | SIGIR 2020  | [PDF](https://par.nsf.gov/servlets/purl/10167818)  | [TensorFlow](https://github.com/safe-graph/DGFraud)  |

| COSIN  | Fraud Detection in Dynamic Interaction Network  | TKDE 2020  | [PDF](https://drive.google.com/file/d/1F6mOqzpkDp_2zhi7vvoZCuWByiFfKs5j/view)  | [N/A]  |

| HMGNN  | Heterogeneous Mini-Graph Neural Network and Its Application to Fraud Invitation Detection  | ICDM 2020  | [PDF](https://ieeexplore.ieee.org/abstract/document/9338325/)  | [TensorFlow](https://github.com/iqiyi/HMGNN)  |

| C-FATH  | Modeling Heterogeneous Graph Network on Fraud Detection: A Community-based Framework with Attention Mechanism  | CIKM 2021  | [PDF](https://dl.acm.org/doi/abs/10.1145/3459637.3482277)  | [N/A]  |

| A  | Graph Regularized Autoencoder and its Application in Unsupervised Anomaly Detection  | TPAMI 2021  | [PDF](https://ieeexplore.ieee.org/abstract/document/9380495/)  | [N/A]  |

| DCI  | Decoupling Representation Learning and Classification for GNN-based Anomaly Detection  | SIGIR 2021  | [PDF](https://xiaojingzi.github.io/publications/SIGIR21-Wang-et-al-decoupled-GNN.pdf)  | [PyTorch](https://github.com/wyl7/DCI-pytorch)  |

| IHGAT  | Intention-aware Heterogeneous Graph Attention Networks for Fraud Transactions Detection  | KDD 2021  | [PDF](https://dl.acm.org/doi/abs/10.1145/3447548.3467142)  | [N/A]  |

| AAGNN  | Subtractive Aggregation for Attributed Network Anomaly Detection  | CIKM 2021  | [PDF](https://dl.acm.org/doi/abs/10.1145/3459637.3482195)  | [PyTorch](https://github.com/betterzhou/AAGNN)  |

| ANEMONE  | ANEMONE: Graph Anomaly Detection with Multi-Scale Contrastive Learning  | CIKM 2021  | [PDF](https://shiruipan.github.io/publication/cikm-21-jin/cikm-21-jin.pdf)  | [PyTorch](https://github.com/GRAND-Lab/ANEMONE)  |

| TDA  | Topological anomaly detection in dynamic multilayer blockchain networks  | ECML PKDD 2021  | [PDF](https://link.springer.com/chapter/10.1007/978-3-030-86486-6_48)  | [R](https://github.com/tdagraphs/tdagraphs)  |

| SkewA  | Graph Fraud Detection Based on Accessibility Score Distributions  | ECML PKDD 2021  | [PDF](https://2021.ecmlpkdd.org/wp-content/uploads/2021/07/sub_851.pdf)  | [C++](https://github.com/minjiyoon/PKDD21-SkewA)  |

| BiDyn  | Bipartite Dynamic Representations for Abuse Detection  | KDD 2021  | [PDF](https://dl.acm.org/doi/abs/10.1145/3447548.3467141)  | [PyTorch](https://github.com/qema/bidyn)  |

| GRC  | Towards Consumer Loan Fraud Detection: Graph Neural Networks with Role-Constrained Conditional Random Field  | AAAI 2021  | [PDF](https://aaai.org/AAAI21Papers/AAAI-6859.XuB.pdf)  | [N/A]  |

|   | Signature-Based Anomaly Detection in Networks  | SDM 2021  | [PDF](https://epubs.siam.org/doi/abs/10.1137/1.9781611976700.13)  | [N/A]  |

| CO-GCN  | Graph Neural Network to Dilute Outliers for Refactoring Monolith Application  | AAAI 2021  | [PDF](https://cdn.aaai.org/ojs/16079/16079-13-19573-1-2-20210518.pdf)  | [PyTorch](https://github.com/utkd/cogcn)  |

| PAMFUL  | A Synergistic Approach for Graph Anomaly Detection With Pattern Mining and Feature Learning  | TNNLS 2021  | [PDF](https://ieeexplore.ieee.org/abstract/document/9525041/)  | [PyTorch](https://github.com/zhao-tong/Graph-Anomaly-Loss)  |

| COMMANDER  | Cross-Domain Graph Anomaly Detection  | TNNLS 2021  | [PDF](https://par.nsf.gov/servlets/purl/10352642)  | [N/A]  |

| GDN  | Graph Neural Network-Based Anomaly Detection in Multivariate Time Series  | AAAI 2021  | [PDF](https://cdn.aaai.org/ojs/16523/16523-13-20017-1-2-20210518.pdf)  | [PyTorch](https://github.com/d-ailin/GDN)  |

| PC-GNN  | Pick and choose: a GNN-based imbalanced learning approach for fraud detection  | WWW 2021  | [PDF](https://ponderly.github.io/pub/PCGNN_WWW2021.pdf)  | [PyTorch](https://github.com/PonderLY/PC-GNN)  |

| GDN  | Few-shot network anomaly detection via cross-network meta-learning  | WWW 2021  | [PDF](https://dl.acm.org/doi/abs/10.1145/3442381.3449922)  | [PyTorch](https://github.com/kaize0409/Meta-GDN_AnomalyDetection)  |

| CoLA  | Anomaly detection on attributed networks via contrastive self-supervised learning  | TNNLS 2021  | [PDF](https://ieeexplore.ieee.org/abstract/document/9395172/)  | [PyTorch](https://github.com/grand-lab/cola)  |

| AEGIS  | Inductive Anomaly Detection on Attributed Networks  | IJCAI 2021  | [PDF](https://dl.acm.org/doi/abs/10.5555/3491440.3491619)  | [N/A]  |

| Turbo  | Turbo: Fraud Detection in Deposit-free Leasing Service via Real-Time Behavior Network Mining  | ICDE 2021  | [PDF](https://ieeexplore.ieee.org/abstract/document/9458859/)  | [N/A]  |

| MetaHG  | Distilling Meta Knowledge on Heterogeneous Graph for Illicit Drug Trafficker Detection on Social Media  | NeurIPS 2021  | [PDF](https://proceedings.neurips.cc/paper_files/paper/2021/file/e234e195f3789f05483378c397db1cb5-Supplemental.pdf)  | [PyTorch](https://github.com/graphprojects/MetaHG)  |

| EnsemFDet  | EnsemFDet: An Ensemble Approach to Fraud Detection based on Bipartite Graph  | ICDE 2021  | [PDF](https://www.researchgate.net/profile/Hao-Zhu-35/publication/338158171_EnsemFDet_An_Ensemble_Approach_to_Fraud_Detection_based_on_Bipartite_Graph/links/604619ff4585154e8c864b34/EnsemFDet-An-Ensemble-Approach-to-Fraud-Detection-based-on-Bipartite-Graph.pdf)  | [N/A]  |

| FRAUDRE  | FRAUDRE: Fraud Detection Dual-Resistant to Graph Inconsistency and Imbalance  | ICDM 2021  | [PDF](https://www.researchgate.net/profile/Chuan-Zhou-3/publication/357512222_FRAUDRE_Fraud_Detection_Dual-Resistant_to_Graph_Inconsistency_and_Imbalance/links/61d18807b8305f7c4b19bd14/FRAUDRE-Fraud-Detection-Dual-Resistant-to-Graph-Inconsistency-and-Imbalance.pdf)  | [PyTorch](https://github.com/FraudDetection/FRAUDRE)  |

| ELAND  | Action Sequence Augmentation for Early Graph-based Anomaly Detection  | CIKM 2021  | [PDF](https://arxiv.org/abs/2010.10016)  | [PyTorch](https://github.com/dm2-nd/eland)  |

| DeFraudNet  | DeFraudNet: An End-to-End Weak Supervision Framework to Detect Fraud in Online Food Delivery  | ECML PKDD 2021  | [PDF](https://2021.ecmlpkdd.org/wp-content/uploads/2021/07/sub_10-1.pdf)  | [N/A]  |

| DIGNN  | The Devil is in the Conflict: Disentangled Information Graph Neural Networks for Fraud Detection  | ICDM 2022  | [PDF](https://arxiv.org/abs/2210.12384)  | [N/A]  |

| STGAN  | Graph Convolutional Adversarial Networks for Spatiotemporal Anomaly Detection  | TNNLS 2022  | [PDF](https://ieeexplore.ieee.org/abstract/document/9669110/)  | [PyTorch](https://github.com/dleyan/STGAN)  |

| DAGAD  | DAGAD: Data Augmentation for Graph Anomaly Detection  | ICDM 2022  | [PDF](https://ieeexplore.ieee.org/abstract/document/10027747/)  | [PyTorch](https://github.com/fanzhenliu/dagad)  |

| NGS  | Explainable Graph-based Fraud Detection via Neural Meta-graph Search  | CIKM 2022  | [PDF](https://ponderly.github.io/pub/NGS_CIKM2022.pdf)  | [N/A]  |

| BRIGHT  | BRIGHT-Graph Neural Networks in Real-time Fraud Detection  | CIKM 2022  | [PDF](https://dl.acm.org/doi/abs/10.1145/3511808.3557136)  | [N/A]  |

| Hetero-SCAN  | Meta-Path-based Fake News Detection Leveraging Multi-level Social Context Information  | CIKM 2022  | [PDF](https://arxiv.org/abs/2109.08022)  | [N/A]  |

| DynAnom  | Subset Node Anomaly Tracking over Large Dynamic Graphs  | KDD 2022  | [PDF](https://openreview.net/pdf?id=Xx7eJDXG3Xl)  | [NetworkX](https://github.com/zjlxgxz/dynanom)  |

| H2-FDetector  | H2-FDetector: A GNN-based Fraud Detector with Homophilic and Heterophilic Connections  | WWW 2022  | [PDF](https://scholar.archive.org/work/fomltdkxnrblndckrapxjyusri/access/wayback/https://dl.acm.org/doi/pdf/10.1145/3485447.3512195)  | [PyTorch](https://github.com/shifengzhao/H2-FDetector)  |

| AO-GNN  | AUC-oriented Graph Neural Network for Fraud Detection  | WWW 2022  | [PDF](https://ponderly.github.io/pub/AOGNN_WWW2022.pdf)  | [N/A]  |

| ComGA  | ComGA: Community-Aware Attributed Graph Anomaly Detection  | WSDM 2022  | [PDF](https://dl.acm.org/doi/abs/10.1145/3488560.3498389)  | [TensorFlow](https://github.com/XuexiongLuoMQ/ComGA)  |

| Sub-CR  | Reconstruction Enhanced Multi-View Contrastive Learning for Anomaly Detection on Attributed Networks  | IJCAI 2022  | [PDF](https://www.ijcai.org/proceedings/2022/0330.pdf)  | [PyTorch](https://github.com/Zjer12/Sub)  |

| LUNAR  | LUNAR: Unifying Local Outlier Detection Methods via Graph Neural Networks  | AAAI 2022  | [PDF](https://cdn.aaai.org/ojs/20629/20629-13-24642-1-2-20220628.pdf)  | [PyTorch](https://github.com/agoodge/lunar)  |

| DVGCRN  | Deep Variational Graph Convolutional Recurrent Network for Multivariate Time Series Anomaly Detection  | ICML 2022  | [PDF](https://scholar.archive.org/work/gsvjxkzpqrch7eru5jcwa7mkcq/access/wayback/https://proceedings.mlr.press/v162/chen22x/chen22x.pdf)  | [PyTorch](https://github.com/BoChenGroup/DVGCRN)  |

| MAD-SGCN  | MAD-SGCN: Multivariate Anomaly Detection with Self-learning Graph Convolutional Networks  | ICDE 2022  | [PDF](https://ieeexplore.ieee.org/abstract/document/9835470/)  | [N/A]  |

| MHGL  | Unseen Anomaly Detection on Networks via Multi-Hypersphere Learning  | SDM 2022  | [PDF](https://www4.comp.polyu.edu.hk/~xiaohuang/docs/Shuang_SDM22.pdf)  | [N/A]  |

| HCM  | Hop-Count Based Self-Supervised Anomaly Detection on Attributed Networks  | ECML PKDD 2022  | [PDF](https://2022.ecmlpkdd.org/wp-content/uploads/2022/09/sub_927.pdf)  | [PyTorch](https://github.com/TienjinHuang/GraphAnomalyDetection)  |

| BWGNN  | Rethinking Graph Neural Networks for Anomaly Detection  | ICML 2022  | [PDF](https://www.researchgate.net/profile/Jia-Li-127/publication/360994234_Rethinking_Graph_Neural_Networks_for_Anomaly_Detection/links/6299d59b6886635d5cbb9bb1/Rethinking-Graph-Neural-Networks-for-Anomaly-Detection.pdf)  | [PyTorch](https://github.com/squareroot3/rethinking-anomaly-detection)  |

| BLS  | Bi-Level Selection via Meta Gradient for Graph-Based Fraud Detection  | DASFAA 2022  | [PDF](https://ponderly.github.io/pub/BLS_DASFAA2022.pdf)  | [N/A]  |

| GraphAD  | GraphAD: A Graph Neural Network for Entity-Wise Multivariate Time-Series Anomaly Detection  | SIGIR 2022  | [PDF](https://arxiv.org/abs/2205.11139)  | [N/A]  |

| GAGA  | Label Information Enhanced Fraud Detection against Low Homophily in Graphs  | WWW 2023  | [PDF](https://arxiv.org/abs/2302.10407)  | [PyTorch](https://github.com/orion-wyc/gaga)  |

| GHRN  | Addressing Heterophily in Graph Anomaly Detection: A Perspective of Graph Spectrum  | WWW 2023  | [PDF](https://hexiangnan.github.io/papers/www23-graphAD.pdf)  | [PyTorch](https://github.com/blacksingular/GHRN)  |

| GDN  | Alleviating Structural Distribution Shift in Graph Anomaly Detection  | WSDM 2023  | [PDF](https://hexiangnan.github.io/papers/wsdm23-GDN.pdf)  | [PyTorch](https://github.com/blacksingular/wsdm_GDN)  |

| CODEtect  | Detecting Anomalous Graphs in Labeled Multi-Graph Databases  | TKDD 2023  | [PDF](https://scholar.archive.org/work/lx4zgpvoezgbbkemvkbywfbhmm/access/wayback/https://dl.acm.org/doi/pdf/10.1145/3533770)  | [N/A]  |

| GCAD  | Subgraph Centralization: A Necessary Step for Graph Anomaly Detection  | SDM 2023  | [PDF](https://epubs.siam.org/doi/abs/10.1137/1.9781611977653.ch79)  | [NumPy](https://github.com/IsolationKernel/Codes)  |

| SAD  | SAD: Semi-Supervised Anomaly Detection on Dynamic Graphs  | IJCAI 2023  | [PDF](https://arxiv.org/abs/2305.13573)  | [NumPy](https://github.com/d10andy/sad)  |

| VGOD  | Unsupervised Graph Outlier Detection: Problem Revisit, New Insight, and Superior Method  | ICDE 2023  | [PDF](https://openreview.net/pdf?id=Kh5gknUMBk)  | [PyTorch](https://github.com/goldennormal/vgod-github)  |

|GRADATE|Graph anomaly detection via multi-scale contrastive learning networks with augmented view|AAAI 2023|[PDF](https://ojs.aaai.org/index.php/AAAI/article/view/25907)|[PyTorch](https://github.com/FelixDJC/GRADATE)|

|CLAD|Class Label-aware Graph Anomaly Detection|CIKM 2023|[PDF](https://dl.acm.org/doi/abs/10.1145/3583780.3615249)|[PyTorch](https://github.com/jhkim611/CLAD)|

|SplitGNN|SplitGNN: Spectral Graph Neural Network for Fraud Detection against Heterophily|CIKM 2023|[PDF](https://dl.acm.org/doi/abs/10.1145/3583780.3615067)|[Pytorch](https://github.com/Split-GNN/SplitGNN)|

|TSAGMM|Unsupervised Fraud Transaction Detection on Dynamic Attributed Networks|DASFAA 2023|[PDF](https://librahu.github.io/data/dasfaa2023_ufdt_paper.pdf)|[N/A]|

||Example-based explanations for streaming fraud detection on graphs|Information Sciences 2023|[PDF](https://www.sciencedirect.com/science/article/pii/S0020025522014232)|[N/A]|

|GDAE|Learning graph deep autoencoder for anomaly detection in multi-attributed networks|KBS 2023|[PDF](https://www.sciencedirect.com/science/article/pii/S0950705122011807)|[N/A]|

|RegraphGAN|RegraphGAN: A graph generative adversarial network model for dynamic network anomaly detection|Neural Networks 2023|[PDF](https://www.sciencedirect.com/science/article/pii/S0893608023003842)|[N/A]|

|PANG|Pattern mining for anomaly detection in graphs: Application to fraud in public procurement|RCMLPKDD 2023|[PDF](https://arxiv.org/pdf/2306.10857)|[Scikit-Learn](https://github.com/compnet/pang)|

|LGM-GNN|LGM-GNN: A local and global aware memory-based graph neural network for fraud detection|TBD 2023|[PDF](https://ieeexplore.ieee.org/abstract/document/10008063/)|[N/A]|

|CFAD|Counterfactual graph learning for anomaly detection on attributed networks|TKDE 2023|[PDF](https://www.researchgate.net/profile/Chunjing-Xiao/publication/368909540_Counterfactual_Graph_Learning_for_Anomaly_Detection_on_Attributed_Networks/links/643a88231b8d044c6327f2ad/Counterfactual-Graph-Learning-for-Anomaly-Detection-on-Attributed-Networks.pdf)|[PyTorch](https://github.com/ChunjingXiao/CFAD)|

|ARISE|ARISE: Graph Anomaly Detection on Attributed Networks via Substructure Awareness|TNNLS 2023|[PDF](https://arxiv.org/pdf/2211.15255)|[PyTorch](https://github.com/FelixDJC/ARISE)|

|SAMCL|SAMCL: Subgraph-Aligned Multiview Contrastive Learning for Graph Anomaly Detection|TNNLS 2023|[PDF](https://ieeexplore.ieee.org/abstract/document/10310240)|[N/A]|

|AER-AD|Anonymous Edge Representation for Inductive Anomaly Detection in Dynamic Bipartite Graph|VLDB 2023|[PDF](https://www.vldb.org/pvldb/vol16/p1154-fang.pdf)|[PyTorch](https://github.com/fanglanting/AER)|

|GDN|Alleviating Structural Distribution Shift in Graph Anomaly Detection|WSDM 2023|[PDF](https://arxiv.org/pdf/2401.14155)|[PyTorch](https://github.com/blacksingular/wsdm_GDN)|

|GHRN|Addressing Heterophily in Graph Anomaly Detection: A Perspective of Graph Spectrum|WWW 2023|[PDF](https://hexiangnan.github.io/papers/www23-graphAD.pdf)|[PyTorch](https://github.com/blacksingular/GHRN)|

|IMINF|Fraud detection on multi-relation graphs via imbalanced and interactive learning|Information Sciences 2023|[PDF](https://www.sciencedirect.com/science/article/pii/S0020025523007387)|[N/A]|

|ADA-GAD|ADA-GAD: Anomaly-Denoised Autoencoders for Graph Anomaly Detection|AAAI 2024|[PDF](https://ojs.aaai.org/index.php/AAAI/article/view/28691)|[PyTorch](https://github.com/jweihe/ADA-GAD)|

|BSL|Barely Supervised Learning for Graph-Based Fraud Detection|AAAI 2024|[PDF](https://ojs.aaai.org/index.php/AAAI/article/view/29593)|[N/A]|

|ACT|Cross-Domain Graph Anomaly Detection via Anomaly-Aware Contrastive Alignment|AAAI 2024|[PDF](https://ojs.aaai.org/index.php/AAAI/article/view/25591)|[PyTorch](https://github.com/QZ-WANG/ACT)|

|SEC-GFD|Revisiting Graph-Based Fraud Detection in Sight of Heterophily and Spectrum|AAAI 2024||[Code](https://github.com/Sunxkissed/SEC-GFD)|

|PB-AAD|Improving Robustness of GNN-based Anomaly Detection by Graph Adversarial Training|COLING 2024|[PDF](https://aclanthology.org/2024.lrec-main.779.pdf)|[N/A]|

|SPGNN|Subgraph Patterns Enhanced Graph Neural Network for Fraud Detection|DASFAA 2024|[PDF](https://link.springer.com/chapter/10.1007/978-981-97-5572-1_26)|[N/A]|

|F2GNN|F2GNN: An Adaptive Filter with Feature Segmentation for Graph-Based Fraud Detection|ICASSP 2024|[PDF](https://ieeexplore.ieee.org/abstract/document/10446523)|[N/A]|

|BOURNE|BOURNE: Bootstrapped Self-supervised Learning Framework for Unified Graph Anomaly Detection|ICDE 2024|[PDF](https://arxiv.org/pdf/2307.15244)|[N/A]|

|DualGAD|DualGAD: Dual-bootstrapped self-supervised learning for graph anomaly detection|Information Sciences 2024|[PDF](https://www.sciencedirect.com/science/article/pii/S002002552400433X)|[N/A]|

|SEFraud|SEFraud: Graph-based Self-Explainable Fraud Detection via Interpretative Mask Learning|KDD 2024|[PDF](https://arxiv.org/pdf/2406.11389)|[N/A]|

|HGIF|Heterophilic Graph Invariant Learning for Out-of-Distribution of Fraud Detection|MM 2024|[PDF](https://openreview.net/pdf?id=JnG3wI5E5P)|[PyTorch](https://github.com/Ling-Fei-Ren/HGIF)|

|GFCN|Graph fairing convolutional networks for anomaly detection|PR 2024|[PDF](https://arxiv.org/pdf/2010.10274)|[PyTorch](https://github.com/MahsaMesgaran/GFCN)|

|MPIN|Multipattern Integrated Networks With Contrastive Pretraining for Graph Anomaly Detection|TCSS 2024|[PDF](https://ieeexplore.ieee.org/abstract/document/10604432)|[N/A]|

|Spade+|Spade+: A Generic Real-Time Fraud Detection Framework on Dynamic Graphs|TKDE 2024|[PDF](https://ieeexplore.ieee.org/abstract/document/10510636)|[N/A]|

|FedCAD|Federated Graph Anomaly Detection via Contrastive Self-Supervised Learning|TNNLS 2024|[PDF](https://ieeexplore.ieee.org/abstract/document/10566052)|[N/A]|

|GAD-NR|GAD-NR: Graph Anomaly Detection via Neighborhood Reconstruction|WSDM 2024|[PDF](https://dl.acm.org/doi/pdf/10.1145/3616855.3635767)|[PyTorch](https://github.com/graph-com/GAD-NR)|

|BioGNN|Graph Anomaly Detection with Bi-level Optimization|WWW 2024|[PDF](https://openreview.net/pdf?id=84szxJZS1w)|[N/A]|

#### 1.1.3 Few-Shot Node Classification

| Name  | Title | Venue | Paper | Code |

| ------------- | ------------- | ------------- | ------------- | ------------- |

| Meta-GNN  | Meta-GNN: On Few-shot Node Classification in Graph Meta-learning  | CIKM 2019  | [PDF](https://arxiv.org/abs/1905.09718)  | [PyTorch](https://github.com/ChengtaiCao/Meta-GNN)  |

| AMM-GNN  | Graph Few-shot Learning with Attribute Matching  | CIKM 2020  | [PDF](https://www.public.asu.edu/~kding9/pdf/CIKM2020_AMM.pdf)  | [N/A]  |

| GFL  | Graph Few-shot Learning via Knowledge Transfer  | AAAI 2020  | [PDF](https://arxiv.org/abs/1910.03053)  | [PyTorch](https://github.com/huaxiuyao/GFL)  |

| GPN  | Graph Prototypical Networks for Few-shot Learning on Attributed Networks  | CIKM 2020  | [PDF](https://arxiv.org/abs/2006.12739)  | [PyTorch](https://github.com/kaize0409/GPN_Graph-Few-shot)  |

| G-Meta  | Graph Meta Learning via Local Subgraphs  | NeurIPS 2020  | [PDF](https://arxiv.org/abs/2006.07889)  | [PyTorch](https://github.com/mims-harvard/G-Meta)  |

| MetaTNE  | Node Classification on Graphs with Few-Shot Novel Labels via Meta Transformed Network Embedding  | NeurIPS 2020  | [PDF](https://arxiv.org/abs/2007.02914)  | [PyTorch](https://github.com/llan-ml/MetaTNE)  |

| RALE  | Relative and Absolute Location Embedding for Few-Shot Node Classification on Graph  | AAAI 2021  | [PDF](https://ojs.aaai.org/index.php/AAAI/article/view/16551)  | [TensorFlow](https://github.com/shuaiOKshuai/RALE)  |

| MuL-GRN  | MuL-GRN: Multi-Level Graph Relation Network for Few-Shot Node Classification  | TKDE 2022  | [PDF](https://ieeexplore.ieee.org/document/9779997)  | [N/A]  |

| ST-GFSL  | Spatio-Temporal Graph Few-Shot Learning with Cross-City Knowledge Transfer  | KDD 2022  | [PDF](https://arxiv.org/abs/2205.13947)  | [PyTorch](https://github.com/RobinLu1209/ST-GFSL)  |

| TENT  | Task-Adaptive Few-shot Node Classification  | KDD 2022  | [PDF](https://arxiv.org/abs/2206.11972)  | [PyTorch](https://github.com/SongW-SW/TENT)  |

| Meta-GPS  | Few-shot Node Classification on Attributed Networks with Graph Meta-learning  | SIGIR 2022  | [PDF](https://dl.acm.org/doi/abs/10.1145/3477495.3531978)  | [N/A]  |

| IA-FSNC  | Information Augmentation for Few-shot Node Classification  | IJCAI 2022  | [PDF](https://www.ijcai.org/proceedings/2022/500)  | 	[N/A]  |

| SGCL  | Supervised Graph Contrastive Learning for Few-shot Node Classification  | ECML-PKDD 2022  | [PDF](https://arxiv.org/abs/2203.15936)  | [N/A]  |

| TLP  | Transductive Linear Probing: A Novel Framework for Few-Shot Node Classification  | LoG 2022  | [PDF](https://arxiv.org/abs/2212.05606)  | [PyTorch](https://github.com/Zhen-Tan-dmml/TLP-FSNC)  |

| Stager  | Generalized Few-Shot Node Classification  | ICDM 2022  | [PDF](https://ieeexplore.ieee.org/document/10027718)  | [PyTorch](https://github.com/pricexu/STAGER)  |

| HAG-Meta  | Graph Few-shot Class-incremental Learning  | WSDM 2022  | [PDF](https://arxiv.org/abs/2112.12819)  | [PyTorch](https://github.com/Zhen-Tan-dmml/GFCIL)  |

| Geometer  | Geometer: Graph Few-Shot Class-Incremental Learning via Prototype Representation  | KDD 2022  | [PDF](https://arxiv.org/abs/2205.13954)  | [PyTorch](https://github.com/RobinLu1209/Geometer)  |

| CrossHG-Meta  | Few-shot Heterogeneous Graph Learning via Cross-domain Knowledge Transfer  | KDD 2022  | [PDF](https://dl.acm.org/doi/abs/10.1145/3534678.3539431)  | [N/A]  |

| HG-Meta  | HG-Meta: Graph Meta-learning over Heterogeneous Graphs  | SDM 2022  | [PDF](https://epubs.siam.org/doi/10.1137/1.9781611977172.45)  | [N/A]  |

| TRGM  | Task-level Relations Modelling for Graph Meta-learning  | ICDM 2022  | [PDF](https://ieeexplore.ieee.org/document/10027781)  | [N/A]  |

| X-FNC  | Few-shot Node Classification with Extremely Weak Supervision  | WSDM 2023  | [PDF](https://arxiv.org/abs/2301.02708)  | [PyTorch](https://github.com/SongW-SW/X-FNC)  |

| GraphPrompt  | GraphPrompt: Unifying Pre-Training and Downstream Tasks for Graph Neural Networks  | WWW 2023  | [PDF](https://arxiv.org/abs/2302.08043)  | [PyTorch](https://github.com/Starlien95/GraphPrompt)  |

||Pseudo-Labeling with Graph Active Learning for Few-shot Node Classification|ICDM 2023|[PDF](https://faculty.ist.psu.edu/wu/papers/Pseudo-Labeling-ICDM2023.pdf)|[N/A]|

|COSMIC|Contrastive Meta-Learning for Few-shot Node Classification|KDD 2023|[PDF](https://dl.acm.org/doi/pdf/10.1145/3580305.3599288)|[PyTorch](https://github.com/SongW-SW/COSMIC)|

|TEG|Task-Equivariant Graph Few-shot Learning|KDD 2023|[PDF](https://arxiv.org/pdf/2305.18758)|[PyTorch](https://github.com/sung-won-kim/TEG)|

|VNT|Virtual Node Tuning for Few-shot Node Classification|KDD 2023|[PDF](https://arxiv.org/pdf/2306.06063)|[N/A]|

|Stager|Generalized few-shot node classification: toward an uncertainty-based solution|KIS 2023|[PDF](https://link.springer.com/article/10.1007/s10115-023-01975-7)|[N/A]|

|Meta-GIN|Robust Graph Meta-learning for Weakly-supervised Few-shot Node Classification|TKDD 2024|[PDF](https://arxiv.org/pdf/2106.06873)|[N/A]|

|COLA|Graph Contrastive Learning Meets Graph Meta Learning: A Unified Method for Few-shot Node Tasks|WWW 2024|[PDF](https://dl.acm.org/doi/pdf/10.1145/3589334.3645367)|[PyTorch](https://github.com/Haoliu-cola/COLA)|

#### 1.1.4 Zero-Shot Node Classification

| Name  | Title | Venue | Paper | Code |

| ------------- | ------------- | ------------- | ------------- | ------------- |

| RSDNE  | RSDNE: Exploring Relaxed Similarity and Dissimilarity from Completely-Imbalanced Labels for Network Embedding  | AAAI 2018  | [PDF](https://ojs.aaai.org/index.php/AAAI/article/view/11242)  | [MATLAB](https://github.com/zhengwang100/RSDNE)  |

| RECT  | Network Embedding with Completely-imbalanced Labels  | TKDE 2020  | [PDF](https://arxiv.org/abs/2007.03545)  | [PyTorch](https://github.com/zhengwang100/RECT)  |

| RECT  | Expanding Semantic Knowledge for Zero-shot Graph Embedding  | DASFAA 2021  | [PDF](https://arxiv.org/abs/2103.12491)  | [N/A]  |

| DGPN  | Zero-shot Node Classification with Decomposed Graph Prototype Network  | KDD 2021  | [PDF](https://arxiv.org/abs/2106.08022)  | [PyTorch](https://github.com/zhengwang100/dgpn)  |

| DBiGCN  | Dual Bidirectional Graph Convolutional Networks for Zero-shot Node Classification  | KDD 2022  | [PDF](http://www.lamda.nju.edu.cn/conf/mla22/paper/yq-KDD2022.pdf)  | [PyTorch](https://github.com/warmerspring/DBiGCN)  |

|MVE|Multi-view enhanced zero-shot node classification|IPM 2023|[PDF](https://www.sciencedirect.com/science/article/pii/S0306457323002169)|[N/A]|

|GraphCEN|Zero-shot Node Classification with Graph Contrastive Embedding Network|TMLR 2023|[PDF](https://openreview.net/pdf?id=8wGXnjRLSy)|[N/A]|

### 1.2 Edge-Level Class Imbalance

#### 1.2.1 Few-Shot Link Prediction

| Name  | Title | Venue | Paper | Code |

| ------------- | ------------- | ------------- | ------------- | ------------- |

| EA-GAT  | Few-Shot Link Prediction for Event-Based Social Networks via Meta-learning  | DASFAA 2023  | [PDF](https://link.springer.com/chapter/10.1007/978-3-031-30675-4_3)  | [N/A]  |

|EA-GAT|Few-Shot Link Prediction for Event-Based Social Networks via Meta-learning|DASFAA 2023|[PDF](http://staff.ustc.edu.cn/~tongxu/Papers/Xi_DASFAA23.pdf)|[PyTorch](https://github.com/xizhu1022/FSLP-EBSNs)|

|MLAN|Meta-learning adaptation network for few-shot link prediction in heterogeneous social networks|IPM 2023|[PDF](https://www.sciencedirect.com/science/article/pii/S0306457323001553)|[N/A]|

#### 1.2.2 Edge-Level Anomaly Detection

| Name  | Title | Venue | Paper | Code |

| ------------- | ------------- | ------------- | ------------- | ------------- |

| AddGraph  | AddGraph: Anomaly Detection in Dynamic Graph Using Attention-based Temporal GCN | IJCAI 2019 | [PDF](https://www.ijcai.org/Proceedings/2019/0614.pdf)  | [PyTorch](https://github.com/Ljiajie/Addgraph)  |

| NEDM  | A Nodes' Evolution Diversity Inspired Method to Detect Anomalies in Dynamic Social Networks | TKDE 2019 | [PDF](https://ieeexplore.ieee.org/document/8695818)  | [N/A]  |

|  | Anomaly detection in the dynamics of web and social networks using associative memory  | WWW 2019 | [PDF](https://arxiv.org/pdf/1901.09688.pdf)  | [N/A]  |

| AANE  | AANE: Anomaly Aware Network Embedding for Anomalous Link Detection  | ICDM 2020 | [PDF](https://ieeexplore.ieee.org/document/9338406)  | [N/A]  |

| StrGNN  | Structural Temporal Graph Neural Networks for Anomaly Detection in Dynamic Graphs  | CIKM 2021  | [PDF](https://arxiv.org/pdf/2005.07427.pdf)  | [PyTorch](https://github.com/LeiCaiwsu/StrGNN)  |

| F-FADE  | F-FADE: Frequency Factorization for Anomaly Detection in Edge Streams  | WSDM 2021  | [PDF](https://arxiv.org/pdf/2011.04723.pdf)  | [PyTorch](https://github.com/snap-stanford/F-FADE)  |

| LIFE  | Live-Streaming Fraud Detection: A Heterogeneous Graph Neural Network Approach  | KDD 2021  | [PDF](https://dl.acm.org/doi/abs/10.1145/3447548.3467065)  | [N/A]  |

| GLAD  | Deep Graph Learning for Anomalous Citation Detection  | TNNLS 2022 | [PDF](https://arxiv.org/abs/2202.11360)  | [N/A] |

| AnoGraph | Sketch-Based Anomaly Detection in Streaming Graphs | KDD 2023 | [PDF](https://dl-acm-org.libproxy1.nus.edu.sg/doi/pdf/10.1145/3580305.3599504)  | [C++](https://github.com/Stream-AD/AnoGraph) |

#### 1.2.3 Imbalanced Link Classification

| Name  | Title | Venue | Paper | Code |

| ------------- | ------------- | ------------- | ------------- | ------------- |

| RIWS  | A Linkage-based Doubly Imbalanced Graph Learning Framework for Face Clustering | SDM 2023 | [PDF](https://arxiv.org/abs/2107.02477)  | [PyTorch](https://github.com/espectre/GCNs_on_imbalanced_datasets) |

### 1.3 Graph-Level Class Imbalance

#### 1.3.1 Imbalanced Graph Classification

| Name  | Title | Venue | Paper | Code |

| ------------- | ------------- | ------------- | ------------- | ------------- |

| $\text{G}^2\text{GNN}$  | Imbalanced Graph Classification via Graph-of-Graph Neural Networks | CIKM 2022  | [PDF](https://arxiv.org/abs/2112.00238)  | [PyTorch](https://github.com/YuWVandy/G2GNN)  |

|SGIR|Semi-Supervised Graph Imbalanced Regression|KDD 2023|[PDF](https://arxiv.org/pdf/2305.12087)|[PyTorch](https://github.com/liugangcode/SGIR)|

#### 1.3.2 Graph-Level Anomaly Detection

| Name  | Title | Venue | Paper | Code |

| ------------- | ------------- | ------------- | ------------- | ------------- |

| STREAMSPOT  | Fast Memory-efficient Anomaly Detection in Streaming Heterogeneous Graphs  | KDD 2016 | [PDF](https://arxiv.org/pdf/1602.04844.pdf)  | [Python](https://git.ece.iastate.edu/yizenov1/stream-graph-anomaly-detection)  |

| Graph-TPP  | Query-Driven Discovery of Anomalous Subgraphs in Attributed Graphs  | IJCAI 2017  | [PDF](https://www.ijcai.org/proceedings/2017/0433.pdf)  | [N/A]  |

| AMEN  | Discovering Communities and Anomalies in Attributed Graphs: Interactive Visual Exploration and Summarization  | TKDD 2018  | [PDF](https://dl.acm.org/doi/10.1145/3139241)  | [N/A] |

|  | Concept Drift and Anomaly Detection in Graph Streams | TNNLS 2018  | [PDF](https://arxiv.org/abs/1706.06941)  | [N/A]  |

| Query-map  | Uncovering specific-shape graph anomalies in attributed graphs  | AAAI 2019  | [PDF](https://ojs.aaai.org/index.php/AAAI/article/view/4483)  | [N/A]  |

| ASD-FT  | Anomaly Subgraph Detection with Feature Transfer  | CIKM 2020  | [PDF](https://dl.acm.org/doi/abs/10.1145/3340531.3411968)  | [N/A]  |

| MTAD-GAT | Multivariate Time-series Anomaly Detection via Graph Attention Network  | ICDM 2020  | [PDF](https://arxiv.org/abs/2009.02040)  | [TensorFlow](https://github.com/mangushev/mtad-gat)  |

| GraphAnoGAN  | GraphAnoGAN: Detecting Anomalous Snapshots from Attributed Graphs  | PKDD 2021 | [PDF](https://arxiv.org/abs/2106.15504)  | [TensorFlow](https://github.com/LCS2-IIITD/GraphAnoGAN-ECMLPKDD21/tree/main)  |

| GLocalKD  | Deep Graph-level Anomaly Detection by Glocal Knowledge Distillation  | WSDM 2022  | [PDF](https://arxiv.org/abs/2112.10063)  | [PyTorch](https://github.com/RongrongMa/GLocalKD)  |

| OCGTL  | Raising the Bar in Graph-level Anomaly Detection  | IJCAI 2022  | [PDF](https://arxiv.org/abs/2205.13845)  | [PyTorch](https://github.com/boschresearch/GraphLevel-AnomalyDetection)  |

| AS-GA  | Unsupervised Deep Subgraph Anomaly Detection  | CIKM 2022  | [PDF](https://ieeexplore.ieee.org/document/10027633)  | [PyTorch](https://github.com/rollingstonezz/subgraph_anomaly_detection_icdm22)  |

| AntiBenford  | AntiBenford Subgraphs: Unsupervised Anomaly Detection in Financial Networks  | KDD 2022  | [PDF](https://arxiv.org/abs/2205.13426)  | [Python](https://github.com/tsourakakis-lab/antibenford-subgraphs)  |

| CNSS  | Calibrated Nonparametric Scan Statistics for Anomalous Pattern Detection in Graphs  | AAAI 2022  | [PDF](https://arxiv.org/abs/2206.12786)  | [N/A]  |

| ACGPMiner  | Efficient Anomaly Detection in Property Graphs  | DASFAA 2023  | [PDF](https://link.springer.com/chapter/10.1007/978-3-031-30675-4_9)  | [N/A]  |

| GmapAD | Towards Graph-level Anomaly Detection via Deep Evolutionary Mapping | KDD 2023 | [PDF](https://dl.acm.org/doi/abs/10.1145/3580305.3599524) | [PyTorch](https://github.com/XiaoxiaoMa-MQ/GmapAD) |

|SIGNET|Towards Self-Interpretable Graph-Level Anomaly Detection|NeurIPS 2023|[PDF](https://proceedings.neurips.cc/paper_files/paper/2023/hash/1c6f06863df46de009a7a41b41c95cad-Abstract-Conference.html)|[Pytorch](https://github.com/yixinliu233/SIGNET)|

|IGAD-CF|Imbalanced Graph-Level Anomaly Detection via Counterfactual Augmentation and Feature Learning|SSDBM 2024|[PDF](https://dl.acm.org/doi/abs/10.1145/3676288.3676292)|[PyTorch](https://github.com/whb605/IGAD-CF)|

#### 1.3.3 Few-Shot Graph Classification

| Name  | Title | Venue | Paper | Code |

| ------------- | ------------- | ------------- | ------------- | ------------- |

| AS-MAML  | Adaptive-Step Graph Meta-Learner for Few-Shot Graph Classification  | CIKM 2020  | [PDF](https://arxiv.org/abs/2003.08246)  | [PyTorch](https://github.com/NingMa-AI/AS-MAML)  |

|   | Few-Shot Learning on Graphs via Super-Classes based on Graph Spectral Measures  | ICLR 2020  | [PDF](https://arxiv.org/abs/2002.12815)  | [PyTorch](https://github.com/chauhanjatin10/GraphsFewShot)  |

| PAR  | Property-Aware Relation Networks for Few-Shot Molecular Property Prediction  | NeurIPS 2021  | [PDF](https://arxiv.org/abs/2107.07994)  | [PyTorch](https://github.com/tata1661/PAR-NeurIPS21)  |

| Meta-MGNN  | Few-Shot Graph Learning for Molecular Property Prediction  | WWW 2021  | [PDF](https://arxiv.org/abs/2102.07916)  | [PyTorch](https://github.com/zhichunguo/Meta-MGNN)  |

| FAITH  | FAITH: Few-Shot Graph Classification with Hierarchical Task Graphs  | IJCAI-ECAI 2022  | [PDF](https://arxiv.org/abs/2205.02435)  | [PyTorch](https://github.com/SongW-SW/FAITH)  |

|   | Metric Based Few-Shot Graph Classification  | LoG 2022  | [PDF](https://arxiv.org/abs/2206.03695)  | [PyTorch](https://github.com/crisostomi/metric-few-shot-graph)  |

|   | Cross-Domain Few-Shot Graph Classification  | AAAI 2022  | [PDF](https://arxiv.org/abs/2201.08265)  | [N/A]  |

| Temp-GFSM  | Meta-Learned Metrics over Multi-Evolution Temporal Graphs  | KDD 2022  | [PDF](https://dl.acm.org/doi/10.1145/3534678.3539313)  | [PyTorch](https://github.com/LiriFang/Temp-GFSM)  |

|   | Graph Neural Network Expressivity and Meta-Learning for Molecular Property Regression  | LoG 2022  | [PDF](https://arxiv.org/abs/2209.13410)  | [N/A]  |

| ADKF-IFT  | Meta-learning Adaptive Deep Kernel Gaussian Processes for Molecular Property Prediction  | ICLR 2023  | [PDF](https://arxiv.org/abs/2205.02708)  | [PyTorch](https://github.com/Wenlin-Chen/ADKF-IFT)  |

| MTA  | Meta-Learning with Motif-based Task Augmentation for Few-Shot Molecular Property Prediction  | SDM 2023  | [PDF](https://epubs.siam.org/doi/10.1137/1.9781611977653.ch91)  | [N/A]  |

|CDTC|Cross-Domain Few-Shot Graph Classification with a Reinforced Task Coordinator|AAAI 2023|[PDF](https://ojs.aaai.org/index.php/AAAI/article/view/25615)|[PyTorch](https://github.com/anonymous202205/CDTC)|

## 2. Structure Imbalance

### 2.1 Node-Level Structure Imbalance

#### 2.1.1 Imbalanced Node Degrees

| Name  | Title | Venue | Paper | Code |

| ------------- | ------------- | ------------- | ------------- | ------------- |

| Demo-Net  | DEMO-Net: Degree-specific Graph Neural Networks for Node and Graph Classification  | KDD 2019  | [PDF](https://arxiv.org/pdf/1906.02319.pdf)  | [TensorFlow](https://github.com/jwu4sml/DEMO-Net)  |

| SL-DSGCN  | Investigating and Mitigating Degree-Related Biases in Graph Convolutional Networks  | CIKM 2020  | [PDF](https://arxiv.org/abs/2006.15643)  | [N/A]  |

| DHGAT  | A Dual Heterogeneous Graph Attention Network to Improve Long-Tail Performance for Shop Search in E-Commerce  | KDD 2020  | [PDF](http://shichuan.org/hin/topic/Similarity%20Measure/KDD2020.A%20Dual%20Heterogeneous%20Graph%20Attention%20Network%20to%20Improve%20Long-Tail%20Performance%20for%20Shop%20Search%20in%20E-Commerce.pdf)  | [N/A]  |

| meta-tail2vec  | Towards Locality-Aware Meta-Learning of Tail Node Embeddings on Networks  | CIKM 2020  | [PDF](https://zemin-liu.github.io/papers/CIKM-20-towards-locality-aware-meta-learning-of-tail-node-embeddings-on-network.pdf)  | [TensorFlow](https://github.com/smufang/meta-tail2vec)  |

| Tail-GNN  | Tail-GNN: Tail-Node Graph Neural Networks  | KDD 2021  | [PDF](https://zemin-liu.github.io/papers/Tail-GNN-KDD-21.pdf)  | [PyTorch](https://github.com/shuaiOKshuai/Tail-GNN)  |

|   | Pre-Training Graph Neural Networks for Cold-Start Users and Items Representation  | WSDM 2021  | [PDF](https://arxiv.org/abs/2012.07064)  | [TensorFlow](https://github.com/jerryhao66/Pretrain-Recsys)  |

| Residual2Vec  | Residual2Vec: Debiasing graph embedding with random graphs  | NeurIPS 2021  | [PDF](https://arxiv.org/abs/2110.07654)  | [PyTorch](https://github.com/skojaku/residual2vec)  |

| CenGCN  | CenGCN: Centralized Convolutional Networks with Vertex Imbalance for Scale-Free Graphs  | TKDE 2022  | [PDF](https://arxiv.org/abs/2202.07826)  | [N/A]  |

| MetaDyGNN  | Few-shot Link Prediction in Dynamic Networks  | WSDM 2022  | [PDF](http://www.shichuan.org/doc/120.pdf)  | [PyTorch](https://github.com/BUPT-GAMMA/MetaDyGNN)  |

| Cold Brew  | Cold Brew: Distilling Graph Node Representations with Incomplete or Missing Neighborhoods  | ICLR 2022  | [PDF](https://arxiv.org/abs/2111.04840)  | [PyTorch](https://github.com/amazon-science/gnn-tail-generalization)  |

| BLADE  | BLADE: Biased Neighborhood Sampling based Graph Neural Network for Directed Graphs  | WSDM 2023  | [PDF](https://dl.acm.org/doi/abs/10.1145/3539597.3570430)  | [N/A]  |

|SAILOR|SAILOR: Structural Augmentation Based Tail Node Representation Learning|KDD 2023|[PDF](https://arxiv.org/pdf/2308.06801)|[PyTorch](https://github.com/Jie-Re/SAILOR)|

#### 2.1.2 Node Topology Imbalance

| Name  | Title | Venue | Paper | Code |

| ------------- | ------------- | ------------- | ------------- | ------------- |

| ReNode  | Topology-Imbalance Learning for Semi-Supervised Node Classification  | NeurIPS 2021  | [PDF](https://arxiv.org/abs/2110.04099)  | [PyTorch](https://github.com/victorchen96/ReNode)  |

| PASTEL  | Position-aware Structure Learning for Graph Topology-imbalance by Relieving Under-reaching and Over-squashing  | CIKM 2022  | [PDF](https://arxiv.org/abs/2208.08302)  | [PyTorch](https://github.com/RingBDStack/PASTEL)  |

| HyperIMBA  | Hyperbolic Geometric Graph Representation Learning for Hierarchy-imbalance Node Classification  | WWW 2023  | [PDF](https://arxiv.org/abs/2304.05059)  | [PyTorch](https://github.com/RingBDStack/HyperIMBA)  |

|QTIAH-GNN|QTIAH-GNN: Quantity and Topology Imbalance-aware Heterogeneous Graph Neural Network for Bankruptcy Prediction|KDD 2023|[PDF](http://home.njustkmg.cn:4056/assets/pdf/publications/Conference%20Papers/QTIAH-GNN_%20Quantity%20and%20Topology%20Imbalance-aware%20Heterogeneous%20Graph%20Neural%20Network%20for%20Bankruptcy%20Prediction.pdf)|[PyTorch](https://github.com/Nekofish-L/QTIAH-GNN)|

#### 2.1.3 Long-Tail Entity Embedding on KGs

| Name  | Title | Venue | Paper | Code |

| ------------- | ------------- | ------------- | ------------- | ------------- |

| GEN  | Learning to Extrapolate Knowledge: Transductive Few-shot Out-of-Graph Link Prediction  | NeurIPS 2020  | [PDF](https://arxiv.org/abs/2006.06648)  | [PyTorch](https://github.com/JinheonBaek/GEN)  |

| DAT  | Degree-Aware Alignment for Entities in Tail  | SIGIR 2020  | [PDF](https://arxiv.org/abs/2005.12132)  | [PyTorch](https://github.com/DexterZeng/DAT)  |

| OKELE  | Open Knowledge Enrichment for Long-tail Entities  | WWW 2020  | [PDF](https://arxiv.org/abs/2002.06397)  | [TensorFlow](https://github.com/nju-websoft/OKELE/)  |

| MaKEr  | Meta-Learning Based Knowledge Extrapolation for Knowledge Graphs in the Federated Setting  | 	IJCAI 2022  | [PDF](https://arxiv.org/abs/2205.04692)  | [PyTorch](https://github.com/zjukg/MaKEr)  |

| MorsE  | Meta-Knowledge Transfer for Inductive Knowledge Graph Embedding  | SIGIR 2022  | [PDF](https://arxiv.org/abs/2110.14170)  | [PyTorch](https://github.com/zjukg/MorsE)  |

| MTKGE  | Meta-Learning Based Knowledge Extrapolation for Temporal Knowledge Graph  | WWW 2023  | [PDF](https://arxiv.org/abs/2302.05640)  | [N/A]  |

| KG-Mixup  | Toward Degree Bias in Embedding-Based Knowledge Graph Completion  | WWW 2023  | [PDF](https://arxiv.org/abs/2302.05044)  | [PyTorch](https://github.com/HarryShomer/KG-Mixup)  |

### 2.2 Edge-Level Structure Imbalance 

#### 2.2.1 Few-Shot Relation Classification

| Name  | Title | Venue | Paper | Code |

| ------------- | ------------- | ------------- | ------------- | ------------- |

| Gmatching  | One-Shot Relational Learning for Knowledge Graphs  | EMNLP 2018  | [PDF](https://arxiv.org/abs/1808.09040)  | [PyTorch](https://github.com/xwhan/One-shot-Relational-Learning)  |

| Proto-HATT  | Hybrid Attention-Based Prototypical Networks for Noisy Few-Shot Relation Classification  | AAAI 2019  | [PDF](https://ojs.aaai.org/index.php/AAAI/article/view/4604)  | [PyTorch](https://github.com/thunlp/HATT-Proto)  |

| MetaR  | Meta Relational Learning for Few-Shot Link Prediction in Knowledge Graphs  | EMNLP 2019  | [PDF](https://arxiv.org/abs/1909.01515)  | [PyTorch](https://github.com/AnselCmy/MetaR)  |

|   | Tackling Long-Tailed Relations and Uncommon Entities in Knowledge Graph Completion  | EMNLP-IJCNLP 2019  | [PDF](https://aclanthology.org/D19-1024/)  | [PyTorch](https://github.com/ZihaoWang/Few-shot-KGC)  |

| FSRL  | Few-Shot Knowledge Graph Completion  | AAAI 2020  | [PDF](https://arxiv.org/abs/1911.11298)  | [PyTorch](https://github.com/chuxuzhang/AAAI2020_FSRL)  |

| FAAN  | Adaptive Attentional Network for Few-Shot Knowledge Graph Completion  | EMNLP 2020  | [PDF](https://aclanthology.org/2020.emnlp-main.131/)  | [PyTorch](https://github.com/JiaweiSheng/FAAN)  |

| Mick  | MICK: A Meta-Learning Framework for Few-shot Relation Classification with Small Training Data  | CIKM 2020  | [PDF](https://arxiv.org/abs/2004.14164)  | [PyTorch](https://github.com/XiaoqingGeng/MICK)  |

| Neural Snowball  | Neural Snowball for Few-Shot Relation Learning  | AAAI 2020  | [PDF](https://arxiv.org/abs/1908.11007)  | [PyTorch](https://github.com/thunlp/Neural-Snowball) |

| REFORM  | REFORM: Error-Aware Few-Shot Knowledge Graph Completion  | CIKM 2021  | [PDF](https://chenannie45.github.io/CIKM21a.pdf)  | [PyTorch](https://github.com/SongW-SW/REFORM)  |

| P-INT  | P-INT: A Path-based Interaction Model for Few-shot Knowledge Graph Completion  | EMNLP 2021  | [PDF](https://aclanthology.org/2021.findings-emnlp.35/)  | [PyTorch](https://github.com/RUCKBReasoning/P-INT)  |

| MetaP  | MetaP: Meta Pattern Learning for One-Shot Knowledge Graph Completion  | SIGIR 2021  | [PDF](https://dl.acm.org/doi/abs/10.1145/3404835.3463086)  | [PyTorch](https://github.com/jzystc/metap)  |

| MTransH  | Relational Learning with Gated and Attentive Neighbor Aggregator for Few-Shot Knowledge Graph Completion  | SIGIR 2021  | [PDF](https://arxiv.org/abs/2104.13095)  | [PyTorch](https://github.com/ngl567/GANA-FewShotKGC)  |

| KEFDA  | Knowledge-Enhanced Domain Adaptation in Few-Shot Relation Classification  | KDD 2021  | [PDF](https://dl.acm.org/doi/abs/10.1145/3447548.3467438)  | [PyTorch](https://github.com/imJiawen/KEFDA)  |

| IAN  | Multi-view Interaction Learning for Few-Shot Relation Classification  | CIKM 2021  | [PDF](https://dl.acm.org/doi/abs/10.1145/3459637.3482280)  | [N/A]  |

| HMNet  | HMNet: Hybrid Matching Network for Few-Shot Link Prediction  | DASFAA 2021  | [PDF](https://link.springer.com/chapter/10.1007/978-3-030-73194-6_21)  | [N/A]  |

| APN-LW-JRL   | Adaptive Prototypical Networks with Label Words and Joint Representation Learning for Few-Shot Relation Classification  | TNNLS 2021  | [PDF](https://arxiv.org/abs/2101.03526)  | [N/A]  |

| GMUC  | Gaussian Metric Learning for Few-Shot Uncertain Knowledge Graph Completion  | DASFAA 2021  | [PDF](https://link.springer.com/chapter/10.1007/978-3-030-73194-6_18)  | [PyTorch](https://github.com/zhangjiatao/GMUC)  |

| FAEA  | Function-words Enhanced Attention Networks for Few-Shot Inverse Relation Classification  | IJCAI 2022  | [PDF](https://arxiv.org/abs/2204.12111)  | [PyTorch](https://github.com/DOU123321/FAEA-FSRC)  |

| MULTIFORM  | MULTIFORM: Few-Shot Knowledge Graph Completion via Multi-modal Contexts  | ECML-PKDD 2022  | [PDF](https://2022.ecmlpkdd.org/wp-content/uploads/2022/09/sub_354.pdf)  | [N/A]  |

| GAPNM  | Granularity-Aware Area Prototypical Network With Bimargin Loss for Few Shot Relation Classification  | TKDE 2022  | [PDF](https://ieeexplore.ieee.org/document/9699028)  | [N/A]  |

| Meta-iKG  | Subgraph-aware Few-Shot Inductive Link Prediction via Meta-Learning  | TKDE 2022  | [PDF](https://arxiv.org/abs/2108.00954)  | [N/A]  |

|   | Improving Few-Shot Relation Classification by Prototypical Representation Learning with Definition Text  | NAACL 2022  | [PDF](https://aclanthology.org/2022.findings-naacl.34/)  | [N/A]  |

| CIAN  | Learning Inter-Entity-Interaction for Few-Shot Knowledge Graph Completion  | EMNLP 2022  | [PDF](https://aclanthology.org/2022.emnlp-main.524/)  | [PyTorch](https://github.com/cjlyl/FKGC-CIAN)  |

| HiRe  | Hierarchical Relational Learning for Few-Shot Knowledge Graph Completion  | ICLR 2023  | [PDF](https://openreview.net/forum?id=zlwBI2gQL3K)  | [PyTorch](https://github.com/alexhw15/HiRe)  |

| NP-FKGC  | Normalizing Flow-based Neural Process for Few-Shot Knowledge Graph Completion  | SIGIR 2023  | [PDF](https://arxiv.org/abs/2304.08183)  | [PyTorch](https://github.com/RManLuo/NP-FKGC)  |

#### 2.2.2 Zero-Shot Relation Classification

| Name  | Title | Venue | Paper | Code |

| ------------- | ------------- | ------------- | ------------- | ------------- |

| ZSGAN  | Generative Adversarial Zero-Shot Relational Learning for Knowledge Graphs  | AAAI 2020  | [PDF](https://arxiv.org/abs/2001.02332)  | [PyTorch](https://github.com/Panda0406/Zero-shot-knowledge-graph-relational-learning)  |

| ZSLRC  | Zero-shot Relation Classification from Side Information  | CIKM 2021  | [PDF](https://arxiv.org/abs/2011.07126)  | [PyTorch](https://github.com/gjiaying/ZSLRC)  |

#### 2.2.3 Few-Shot Reasoning on KGs

| Name  | Title | Venue | Paper | Code |

| ------------- | ------------- | ------------- | ------------- | ------------- |

| Meta-KGR  | Adapting Meta Knowledge Graph Information for Multi-Hop Reasoning over Few-Shot Relations  | EMNLP-IJCNLP 2019  | [PDF](https://aclanthology.org/D19-1334/)  | [PyTorch](https://github.com/THU-KEG/MetaKGR)  |

| FIRE  | Few-Shot Multi-Hop Relation Reasoning over Knowledge Bases  | EMNLP 2020  | [PDF](https://aclanthology.org/2020.findings-emnlp.51/)  | [N/A]  |

| THML  | When Hardness Makes a Difference: Multi-Hop Knowledge Graph Reasoning over Few-Shot Relations  | CIKM 2021  | [PDF](https://dl.acm.org/doi/10.1145/3459637.3482402)  | [N/A]  |

| ADK-KG  | Adapting Distilled Knowledge for Few-shot Relation Reasoning over Knowledge Graphs  | SDM 2022  | [PDF](https://epubs.siam.org/doi/10.1137/1.9781611977172.75)  | [PyTorch](https://github.com/ADK-KG/ADK-KG)  |

### 2.3 Graph-Level Structure Imbalance

#### 2.3.1 Imbalanced Graph Sizes

| Name  | Title | Venue | Paper | Code |

| ------------- | ------------- | ------------- | ------------- | ------------- |

| SOLT-GNN  | On Size-Oriented Long-Tailed Graph Classification of Graph Neural Networks  | WWW 2022  | [PDF](https://zemin-liu.github.io/papers/SOLT-GNN-WWW-22.pdf)  | [PyTorch](https://github.com/shuaiOKshuai/SOLT-GNN)  |

#### 2.3.2 Imbalanced Topology Groups

| Name  | Title | Venue | Paper | Code |

| ------------- | ------------- | ------------- | ------------- | ------------- |

| TopoImb  | TopoImb: Toward Topology-level Imbalance in Learning from Graphs  | LoG 2022  | [PDF](https://arxiv.org/abs/2212.08689)  | [N/A]  |

## 3. Other Related Literature

### 3.1 Fairness Learning on Graphs

| Name  | Title | Venue | Paper | Code |

| ------------- | ------------- | ------------- | ------------- | ------------- |

| CFC  | Compositional fairness constraints for graph embeddings  |  ICML 2019  | [PDF](https://arxiv.org/abs/1905.10674)  | [PyTorch](https://github.com/joeybose/Flexible-Fairness-Constraints)  |

|   | Exploring algorithmic fairness in robust graph covering problems  | NIPS 2019  | [PDF](https://arxiv.org/abs/2006.06865)  | [PyTorch](https://github.com/Aida-Rahmattalabi/FairGraphCovering)  |

| Fairwalk  | Fairwalk: Towards fair graph embedding  | IJCAI 2019  | [PDF](https://www.ijcai.org/proceedings/2019/456)  | [Python](https://github.com/EnderGed/Fairwalk)  |

|   | Spectral relaxations and fair densest subgraphs  | CIKM 2020  | [PDF](https://dl.acm.org/doi/10.1145/3340531.3412036)  | [N/A]  |

|   | Fairness-aware explainable recommendation over knowledge graphs  | SIGIR 2020  | [PDF](https://dl.acm.org/doi/abs/10.1145/3397271.3401051)  | [N/A](https://github.com/zuohuif/FairKG4Rec)  |

| MaxFair  | On the information unfairness of social networks  | SDM 2020 | [PDF](https://epubs.siam.org/doi/abs/10.1137/1.9781611976236.69)  | [N/A]  |

| FairGNN  | Say no to the discrimination: Learning fair graph neural networks with limited sensitive attribute information  | WSDM 2021  | [PDF](https://arxiv.org/abs/2009.01454)  | [PyTorch](https://github.com/EnyanDai/FairGNN)  |

| InFoRM  | Inform: Individual fairness on graph mining  | KDD 2020  | [PDF](https://dl.acm.org/doi/abs/10.1145/3394486.3403080)  | [Python](https://github.com/jiank2/inform)  |

| DeBayes  | Debayes: a bayesian method for debiasing network embeddings  | ICML 2020  | [PDF](https://arxiv.org/abs/2002.11442)  | [Python](https://github.com/aida-ugent/DeBayes)  |

| MLSD  | Fairness in network representation by latent structural heterogeneity in observational data  | AAAI 2020  | [PDF](https://ojs.aaai.org/index.php/AAAI/article/view/5792)  | [N/A] |

| REDRESS  | Individual fairness for graph neural networks: A ranking based approach  | KDD 2021  | [PDF](https://dl.acm.org/doi/10.1145/3447548.3467266)  | [TensorFlow](https://github.com/yushundong/REDRESS)  |

| FairGAE  | Fair graph auto-encoder for unbiased graph representations with wasserstein distance  | ICDM 2021  | [PDF](https://ieeexplore.ieee.org/document/9679109)  | [N/A]  |

| MCCNIFTY  | A multi-view confidence-calibrated framework for fair and stable graph representation learning  | ICDM 2021  | [PDF](https://ieeexplore.ieee.org/document/9679093)  | [N/A]  |

|  | Certification and trade-off of multiple fairness criteria in graph-based spam detection  | CIKM 2021  | [PDF](https://dl.acm.org/doi/abs/10.1145/3459637.3482325)  | [N/A]  |

| Fairness-Aware PageRank  | Fairness-aware pagerank  | WWW 2021 | [PDF](https://arxiv.org/abs/2005.14431)  | [Python & C++](https://github.com/SotirisTsioutsiouliklis/FairLaR)  |

| FairAdj  | On dyadic fairness: Exploring and mitigating bias in graph connections  | ICLR 2021  | [PDF](https://openreview.net/forum?id=xgGS6PmzNq6)  | [PyTorch](https://github.com/brandeis-machine-learning/FairAdj)  |

|  | Subgroup generalization and fair- ness of graph neural networks | NIPS 2021  | [PDF](https://arxiv.org/abs/2106.15535)  | [PyTorch](https://github.com/TheaperDeng/GNN-Generalization-Fairness)  |

| MMSS  | Socially fair mitigation of misinformation on social networks via constraint stochastic optimization  | AAAI 2022  | [PDF](https://arxiv.org/abs/2203.12537)  | [Python](https://github.com/Ahmed-Abouzeid/MMSS)  |

| CrossWalk  | Crosswalk: Fairness-enhanced node representation learning  | AAAI 2022  | [PDF](https://arxiv.org/abs/2105.02725)  | [Scikit-Learn](https://github.com/ahmadkhajehnejad/CrossWalk)  |

| FairDrop  | Fairdrop: Biased edge dropout for enhancing fairness in graph representation learning  | IEEE Trans. Artif. Intell. 2022  | [PDF](https://arxiv.org/abs/2104.14210)  | [PyTorch](https://github.com/ispamm/FairDrop)  |

| FairVGNN  | Improving fairness in graph neural networks via mitigating sensitive attribute leakage  | KDD 2022  | [PDF](https://arxiv.org/abs/2206.03426)  | [PyTorch](https://github.com/YuWVandy/FairVGNN)  |

| GUIDE  | Guide: Group equality informed individual fairness in graph neural networks  | KDD 2022  | [PDF](https://dl.acm.org/doi/abs/10.1145/3534678.3539346)  | [PyTorch](https://github.com/weihaosong/GUIDE)  |

| REFEREE  | On structural explanation of bias in graph neural networks  | KDD 2022  | [PDF](https://arxiv.org/abs/2206.12104)  | [PyTorch](https://github.com/yushundong/REFEREE)  |

| UD-GNN  | UD-GNN: uncertainty-aware debiased training on semi-homophilous graphs  | KDD 2022  | [PDF](https://dl.acm.org/doi/10.1145/3534678.3539483)  | [N/A](https://github.com/PonderLY/UD-GNN)  |

| GEAR  | Learning fair node representations with graph counterfactual fairness  | WSDM 2022 | [PDF](https://arxiv.org/abs/2201.03662)  | [PyTorch](https://github.com/jma712/gear)  |

| EDITS  | Edits: Modeling and mitigating data bias for graph neural networks  | WWW 2022  | [PDF](https://arxiv.org/abs/2108.05233)  | [PyTorch](https://github.com/yushundong/EDITS)  |

| UGE  | Unbiased graph embedding with biased graph observations  | WWW 2022  | [PDF](https://arxiv.org/abs/2110.13957)  | [PyTorch](https://github.com/MyTHWN/UGE-Unbiased-Graph-Embedding)  |

|   | Adversarial inter-group link injection degrades the fairness of graph neural networks  | ICDM 2022  | [PDF](https://arxiv.org/abs/2209.05957)  | [PyTorch](https://github.com/mengcao327/attack-gnn-fairness)  |

| BA-GNN  | Ba-gnn:On learning bias-aware graph neural network  | ICDE 2022  | [PDF](https://ieeexplore.ieee.org/document/9835653)  | [N/A]  |

| FairAC  | Fair attribute completion on graph with missing attributes  | ICLR 2023  | [PDF](https://arxiv.org/abs/2302.12977)  | [PyTorch](https://github.com/donglgcn/FairAC)  |

| Graphair  | Learning fair graph representations via automated data augmentations  | ICLR 2023  | [PDF](https://openreview.net/forum?id=1_OGWcP1s9w)  | [PyTorch](https://github.com/divelab/DIG)  |

| FAIRTCIM  | On the fairness of time-critical influence maximization in social networks  | TKDE 2023  | [PDF](https://arxiv.org/abs/1905.06618)  | [N/A] |

| CGF  | Path-specific causal fair prediction via auxiliary graph structure learning  | WWW 2023  | [PDF](https://dl.acm.org/doi/abs/10.1145/3543507.3583280)  | [N/A]  |

| F-SEGA  | Fairness-aware clique-preserving spectral clustering of temporal graphs  | WWW 2023  | [PDF](https://dl.acm.org/doi/10.1145/3543507.3583423)  | [N/A](https://github.com/DongqiFu/F-SEGA)  |

| G-Fame  | Fair graph representation learning via diverse mixture-of-experts  | WWW 2023  | [PDF](https://dl.acm.org/doi/abs/10.1145/3543507.3583207)  | [N/A]  |

| RELIANT  | RELIANT: Fair Knowledge Distillation for Graph Neural Networks  | ICDM 2023  | [PDF](https://arxiv.org/abs/2301.01150)  | [PyTorch](https://github.com/yushundong/reliant)  |

# Acknowledgements

This page is contributed and maintained by [Zemin Liu](https://zemin-liu.github.io/) ([email protected]), Yuan Li ([email protected]), and Nan Chen ([email protected]). If you have any suggestions or questions, please feel free to contact us.