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https://github.com/tsinghua-fib-lab/GNN-Recommender-Systems

An index of recommendation algorithms that are based on Graph Neural Networks. (TORS)
https://github.com/tsinghua-fib-lab/GNN-Recommender-Systems
gcn gnn graph-convolutional-networks graph-neural-networks graph-representation-learning information-retrieval recommendation recommendation-algorithms recommendation-system recommender-system
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An index of recommendation algorithms that are based on Graph Neural Networks. (TORS)
Host: GitHub
URL: https://github.com/tsinghua-fib-lab/GNN-Recommender-Systems
Owner: tsinghua-fib-lab
Created: 2021-09-23T07:25:11.000Z (over 3 years ago)
Default Branch: main
Last Pushed: 2022-12-17T01:33:25.000Z (about 2 years ago)
Last Synced: 2024-08-03T23:25:20.292Z (6 months ago)
Topics: gcn, gnn, graph-convolutional-networks, graph-neural-networks, graph-representation-learning, information-retrieval, recommendation, recommendation-algorithms, recommendation-system, recommender-system
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- Readme: README.md
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README

        # GNN based Recommender Systems

An index of recommendation algorithms that are based on Graph Neural Networks.

Our survey **A Survey of Graph Neural Networks for Recommender Systems: Challenges, Methods, and Directions** is accepted by ACM Transactions on Recommender Systems.

A preprint is available on arxiv: [link](https://arxiv.org/pdf/2109.12843v2.pdf)

Please cite our survey paper if this index is helpful.

```

@article{gao2022survey,

  title={A Survey of Graph Neural Networks for Recommender Systems: Challenges, Methods, and Directions},

  author={Gao, Chen and Zheng, Yu and Li, Nian and Li, Yinfeng and Qin, Yingrong and Piao, Jinghua and Quan, Yuhan and Chang, Jianxin and Jin, Depeng and He, Xiangnan and Li, Yong},

  journal={ACM Transactions on Recommender Systems (TORS)},

  year={2022}

}

```

```

Gao, C., Zheng, Y., Li, N., Li, Y., Qin, Y., Piao, J., Quan, Y., Chang, J., Jin, D., He, X., & Li, Y. (2022). A Survey of Graph Neural Networks for Recommender Systems: Challenges, Methods, and Directions. ACM Transactions on Recommender Systems (TORS).

```

# Table of Contents

- [GNN in different recommendation stages](#Recommendation-Stages)

   - [Matching](#Matching)

   - [Ranking](#Ranking)

   - [Re-ranking](#Re-ranking)

- [GNN in different recommendation scenarios](#Recommendation-Scenarios)

   - [Social Recommendation](#Social-Recommendation)

   - [Sequential Recommendation](#Sequential-Recommendation)

   - [Session Recommendation](#Session-Recommendation)

   - [Bundle Recommendation](#Bundle-Recommendation)

   - [Cross Domain Recommendation](#Cross-Domain-Recommendation)

- [GNN for different recommendation objectives](#Recommendation-Objectives)

   - [Multi-behavior Recommendation](#Multi-behavior-Recommendation)

   - [Diversity](#Diversity)

   - [Explainability](#Explainability)

   - [Fairness](#Fairness)

## Recommendation Stages

### Matching

| **Name** | **Paper** | **Venue** | **Year** | **Code** |

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

| GCMC | [Berg, R. V. D., Kipf, T. N., & Welling, M. (2017). Graph convolutional matrix completion. _arXiv preprint arXiv:1706.02263_.](https://arxiv.org/pdf/1706.02263.pdf) | arxiv | 2017 | [Python](https://paperswithcode.com/paper/graph-convolutional-matrix-completion) |

| Pin-Sage | [Ying, R., He, R., Chen, K., Eksombatchai, P., Hamilton, W. L., & Leskovec, J. (2018, July). Graph convolutional neural networks for web-scale recommender systems. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (pp. 974-983).](https://arxiv.org/pdf/1806.01973) | KDD | 2018 | [Python](https://paperswithcode.com/paper/graph-convolutional-neural-networks-for-web) |

| NGCF | [Wang, X., He, X., Wang, M., Feng, F., & Chua, T. S. (2019, July). Neural graph collaborative filtering. In _Proceedings of the 42nd international ACM SIGIR conference on Research and development in Information Retrieval_ (pp. 165-174).](https://arxiv.org/pdf/1905.08108.pdf) | SIGIR | 2019 | [Python](https://paperswithcode.com/paper/neural-graph-collaborative-filtering) |

| LightGCN | [He, X., Deng, K., Wang, X., Li, Y., Zhang, Y., & Wang, M. (2020, July). Lightgcn: Simplifying and powering graph convolution network for recommendation. In _Proceedings of the 43rd International ACM SIGIR conference on research and development in Information Retrieval_ (pp. 639-648).](https://arxiv.org/pdf/2002.02126.pdf) | SIGIR | 2020 | [Python](https://paperswithcode.com/paper/lightgcn-simplifying-and-powering-graph) |

| NIA-GCN | [Sun, J., Zhang, Y., Guo, W., Guo, H., Tang, R., He, X., ... & Coates, M. (2020, July). Neighbor interaction aware graph convolution networks for recommendation. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 1289-1298).](https://dl.acm.org/doi/abs/10.1145/3397271.3401123) | SIGIR | 2020 | NA |

| DGCF | [Wang, X., Jin, H., Zhang, A., He, X., Xu, T., & Chua, T. S. (2020, July). Disentangled graph collaborative filtering. In _Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval_ (pp. 1001-1010).](https://arxiv.org/pdf/2007.01764) | SIGIR | 2020 | [Python](https://paperswithcode.com/paper/disentangled-graph-collaborative-filtering) |

| IMP-GCN | [Liu, F., Cheng, Z., Zhu, L., Gao, Z., & Nie, L. (2021, April). Interest-aware message-passing gcn for recommendation. In Proceedings of the Web Conference 2021 (pp. 1296-1305).](https://arxiv.org/pdf/2102.10044.pdf) | WWW | 2021 | [Python](https://github.com/liufancs/IMP_GCN) |

| SGL | [Wu, J., Wang, X., Feng, F., He, X., Chen, L., Lian, J., & Xie, X. (2021, July). Self-supervised graph learning for recommendation. In _Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval_ (pp. 726-735).](https://arxiv.org/pdf/2010.10783) | SIGIR | 2021 | [Python](https://github.com/wujcan/SGL) |

| LT-OCF | [Choi, J., Jeon, J., & Park, N. (2021). LT-OCF: Learnable-Time ODE-based Collaborative Filtering. In _Proceedings of the 30th ACM International Conference on Information and Knowledge Management_ (pp. 251-260).](https://dl.acm.org/doi/10.1145/3459637.3482449) | CIKM | 2021 | [Python](https://github.com/jeongwhanchoi/LT-OCF) |

| HMLET | [Kong, T., Kim, T., Jeon, J., Choi, J., Lee, Y-C.,Park, N., & Kim, S-W. (2022). Linear, or Non-Linear, That is the Question! In _Proceedings of the 15th ACM International Web Search and Data Mining Conference_ (pp. 517-525).](https://dl.acm.org/doi/abs/10.1145/3488560.3498501) | WSDM | 2022 | [Python](https://github.com/jeongwhanchoi/HMLET) | 

| HS-GCN | [Liu, H., Wei, Y., Yin, J., & Nie, L. (2022). HS-GCN: Hamming Spatial Graph Convolutional Networks for Recommendation. IEEE Transactions on Knowledge and Data Engineering.](https://ieeexplore.ieee.org/document/9732648) | TKDE | 2022 | [Python](https://github.com/hanliu95/HS-GCN) |

| LGCN | [Yu, W., Zhang, Z., & Qin, Z. (2022). Low-pass Graph Convolutional Network for Recommendation.](https://www.aaai.org/AAAI22Papers/AAAI-3643.WenhuiY.pdf) | AAAI | 2022 | [Python](https://github.com/Wenhui-Yu/LCFN) |

### Ranking

| **Name** | **Paper** | **Venue** | **Year** | **Code** |

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

| Fi-GNN | [Li, Z., Cui, Z., Wu, S., Zhang, X., & Wang, L. (2019, November). Fi-gnn: Modeling feature interactions via graph neural networks for ctr prediction. In _Proceedings of the 28th ACM International Conference on Information and Knowledge Management_ (pp. 539-548).](https://arxiv.org/pdf/1910.05552.pdf) | CIKM | 2019 | [Python](https://paperswithcode.com/paper/fi-gnn-modeling-feature-interactions-via) |

| PUP | [Zheng, Y., Gao, C., He, X., Li, Y., & Jin, D. (2020, April). Price-aware recommendation with graph convolutional networks. In _2020 IEEE 36th International Conference on Data Engineering (ICDE)_ (pp. 133-144). IEEE.](https://arxiv.org/pdf/2003.03975.pdf) | ICDE | 2020 | [Python](https://github.com/DavyMorgan/ICDE20-PUP) |

|A2-GCN | [Liu, F., Cheng, Z., Zhu, L., Liu, C., & Nie, L. (2020). A2-GCN: An attribute-aware attentive GCN model for recommendation. IEEE Transactions on Knowledge and Data Engineering.](https://arxiv.org/pdf/2003.09086.pdf) | TKDE | 2020 | NA |

| L₀-SIGN | [Su, Y., Zhang, R., Erfani, S., & Xu, Z. (2021, May). Detecting Beneficial Feature Interactions for Recommender Systems. In _Proceedings of the 34th AAAI Conference on Artificial Intelligence (AAAI)_.](https://arxiv.org/pdf/2008.00404.pdf) | AAAI | 2021 | [Python](https://github.com/ruizhang-ai/SIGN-Detecting-Beneficial-Feature-Interactions-for-Recommender-Systems) |

| DG-ENN | [Guo, W., Su, R., Tan, R., Guo, H., Zhang, Y., Liu, Z., ... & He, X. (2021). Dual Graph enhanced Embedding Neural Network for CTRPrediction. _arXiv preprint arXiv:2106.00314_.](https://arxiv.org/pdf/2106.00314.pdf) | KDD | 2021 | NA |

| SHCF | [Li, C., Hu, L., Shi, C., Song, G., & Lu, Y. (2021). Sequence-aware Heterogeneous Graph Neural Collaborative Filtering. In _Proceedings of the 2021 SIAM International Conference on Data Mining (SDM)_ (pp. 64-72). Society for Industrial and Applied Mathematics.](http://www.shichuan.org/doc/98.pdf) | SDM | 2021 | [Python](http://www.shichuan.org/dataset/SHCF.zip) |

| GCM | [Wu, J., He, X., Wang, X., Wang, Q., Chen, W., Lian, J., & Xie, X. (2020). Graph Convolution Machine for Context-aware Recommender System. _arXiv preprint arXiv:2001.11402_.](https://arxiv.org/pdf/2001.11402.pdf) | Frontiers of Computer Science | 2021 | [Python](https://github.com/wujcan/GCM) |

| TGIN | [Jiang, W., Jiao, Y., Wang, Q., Liang, C., Guo, L., Zhang, Y., ... & Zhu, Y. (2022, February). Triangle Graph Interest Network for Click-through Rate Prediction. In Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining (pp. 401-409).](https://arxiv.org/pdf/2202.02698.pdf) | WSDM | 2022 | [Python](https://github.com/alibaba/tgin) |

### Re-ranking

| **Name** | **Paper** | **Venue** | **Year** | **Code** |

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

| IRGPR | [Liu, W., Liu, Q., Tang, R., Chen, J., He, X., & Heng, P. A. (2020, October). Personalized Re-ranking with Item Relationships for E-commerce. In _Proceedings of the 29th ACM International Conference on Information & Knowledge Management_ (pp. 925-934).](https://dl.acm.org/doi/abs/10.1145/3340531.3412332) | CIKM | 2020 | NA |

## Recommendation Scenarios

### Social Recommendation

| **Name** | **Paper** | **Venue** | **Year** | **Code** |

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

| DiffNet | [Wu, L., Sun, P., Fu, Y., Hong, R., Wang, X., & Wang, M. (2019, July). A neural influence diffusion model for social recommendation. In _Proceedings of the 42nd international ACM SIGIR conference on research and development in information retrieval_ (pp. 235-244).](https://arxiv.org/pdf/1904.10322) | SIGIR | 2019 | [Python](https://paperswithcode.com/paper/a-neural-influence-diffusion-model-for-social) |

| GraphRec | [Fan, W., Ma, Y., Li, Q., He, Y., Zhao, E., Tang, J., & Yin, D. (2019, May). Graph neural networks for social recommendation. In _The World Wide Web Conference_ (pp. 417-426).](https://arxiv.org/pdf/1902.07243) | WWW | 2019 | [Python](https://paperswithcode.com/paper/graph-neural-networks-for-social) |

| DANSER | [Wu, Q., Zhang, H., Gao, X., He, P., Weng, P., Gao, H., & Chen, G. (2019, May). Dual graph attention networks for deep latent representation of multifaceted social effects in recommender systems. In _The World Wide Web Conference_ (pp. 2091-2102).](https://arxiv.org/pdf/1903.10433) | WWW | 2019 | [Python](https://github.com/qitianwu/DANSER-WWW-19) |

| DGRec | [Song, W., Xiao, Z., Wang, Y., Charlin, L., Zhang, M., & Tang, J. (2019, January). Session-based social recommendation via dynamic graph attention networks. In _Proceedings of the Twelfth ACM international conference on web search and data mining_ (pp. 555-563).](https://arxiv.org/pdf/1902.09362) | WSDM | 2019 | [Python](https://github.com/DeepGraphLearning/RecommenderSystems) |

| HGP | [Kim, K. M., Kwak, D., Kwak, H., Park, Y. J., Sim, S., Cho, J. H., ... & Ha, J. W. (2019). Tripartite heterogeneous graph propagation for large-scale social recommendation. _arXiv preprint arXiv:1908.02569_.](https://arxiv.org/pdf/1908.02569) | RecSys | 2019 | NA |

| DiffNet++ | [Wu, L., Li, J., Sun, P., Hong, R., Ge, Y., & Wang, M. (2020). Diffnet++: A neural influence and interest diffusion network for social recommendation. IEEE Transactions on Knowledge and Data Engineering.](https://arxiv.org/pdf/2002.00844.pdf) | TKDE | 2020 | [Python](https://paperswithcode.com/paper/diffnet-a-neural-influence-and-interest)

| MHCN | [Yu, J., Yin, H., Li, J., Wang, Q., Hung, N. Q. V., & Zhang, X. (2021, April). Self-Supervised Multi-Channel Hypergraph Convolutional Network for Social Recommendation. In _Proceedings of the Web Conference 2021_ (pp. 413-424).](https://arxiv.org/pdf/2101.06448) | WWW | 2021 | [Python](https://github.com/Coder-Yu/QRec) |

| SEPT | [Yu, J., Yin, H., Gao, M., Xia, X., Zhang, X., & Hung, N. Q. V. (2021). Socially-Aware Self-Supervised Tri-Training for Recommendation. _arXiv preprint arXiv:2106.03569_.](https://arxiv.org/pdf/2106.03569) | KDD | 2021 | [Python](https://github.com/Coder-Yu/QRec) |

| GBGCN | [Zhang, J., Gao, C., Jin, D., & Li, Y. (2021, April). Group-Buying Recommendation for Social E-Commerce. In _2021 IEEE 37th International Conference on Data Engineering (ICDE)_ (pp. 1536-1547). IEEE.](https://arxiv.org/pdf/2010.06848) | ICDE | 2021 | [Python](https://github.com/Sweetnow/group-buying-recommendation) |

| KCGN | [Huang, C., Xu, H., Xu, Y., Dai, P., Xia, L., Lu, M., ... & Ye, Y. (2021, January). Knowledge-aware coupled graph neural network for social recommendation. In _AAAI Conference on Artificial Intelligence (AAAI)_.](https://www.aaai.org/AAAI21Papers/AAAI-9069.HuangC.pdf) | AAAI | 2021 | [Python](https://github.com/xhcdream/KCGN) |

| DiffNetLG | [Song, C., Wang, B., Jiang, Q., Zhang, Y., He, R., & Hou, Y. (2021, July). Social Recommendation with Implicit Social Influence. In _Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval_ (pp. 1788-1792).](https://dl.acm.org/doi/abs/10.1145/3404835.3463043) | SIGIR | 2021 | NA |

| RecoGCN | [Xu, F., Lian, J., Han, Z., Li, Y., Xu, Y., & Xie, X. (2019, November). Relation-aware graph convolutional networks for agent-initiated social e-commerce recommendation. In _Proceedings of the 28th ACM international conference on information and knowledge management_ (pp. 529-538).](https://dl.acm.org/doi/10.1145/3357384.3357924) | CIKM | 2019 | [Python](https://github.com/xfl15/RecoGCN) |

| GAT-NSR | [Mu, N., Zha, D., He, Y., & Tang, Z. (2019, November). Graph attention networks for neural social recommendation. In _2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI)_ (pp. 1320-1327). IEEE.](https://ieeexplore.ieee.org/abstract/document/8995280) | ICTAI | 2019 | NA |

| SR-HGNN | [Xu, H., Huang, C., Xu, Y., Xia, L., Xing, H., & Yin, D. (2020, November). Global context enhanced social recommendation with hierarchical graph neural networks. In _2020 IEEE International Conference on Data Mining (ICDM)_ (pp. 701-710). IEEE.](https://ieeexplore.ieee.org/abstract/document/9338365) | ICDM | 2020 | [Python](https://github.com/xhcdream/SR-HGNN) |

| TGRec | [Bai, T., Zhang, Y., Wu, B., & Nie, J. Y. (2020, December). Temporal Graph Neural Networks for Social Recommendation. In _2020 IEEE International Conference on Big Data (Big Data)_ (pp. 898-903). IEEE.](https://ieeexplore.ieee.org/abstract/document/9378444) | ICBD | 2020 | NA |

| ESRF | [Yu, J., Yin, H., Li, J., Gao, M., Huang, Z., & Cui, L. (2020). Enhance social recommendation with adversarial graph convolutional networks. _IEEE Transactions on Knowledge and Data Engineering_.](https://arxiv.org/pdf/2004.02340) | TKDE | 2020 | [Python](https://github.com/Coder-Yu/QRec) |

| HOSR | [Liu, Y., Liang, C., He, X., Peng, J., Zheng, Z., & Tang, J. (2020). Modelling high-order social relations for item recommendation. _IEEE Transactions on Knowledge and Data Engineering_.](https://arxiv.org/pdf/2003.10149) | TKDE | 2020 | NA |

| GNN-SoR | [Guo, Z., & Wang, H. (2020). A deep graph neural network-based mechanism for social recommendations. _IEEE Transactions on Industrial Informatics_, _17_(4), 2776-2783.](https://ieeexplore.ieee.org/abstract/document/9063418) | TII | 2020 | NA |

| ASR | [Luo, D., Bian, Y., Zhang, X., & Huan, J. (2020). Attentive Social Recommendation: Towards User And Item Diversities. _arXiv preprint arXiv:2011.04797_.](https://arxiv.org/pdf/2011.04797) | arxiv | 2020 | [Python](https://github.com/flyingdoog/ASR) |

### Sequential Recommendation

| **Name** | **Paper** | **Venue** | **Year** | **Code** |

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

| ISSR | [Liu, F., Liu, W., Li, X., & Ye, Y. (2020). Inter-sequence Enhanced Framework for Personalized Sequential Recommendation. _arXiv preprint arXiv:2004.12118_.](https://arxiv.org/pdf/2004.12118) | AAAI | 2020 | NA |

| MA-GNN | [Ma, C., Ma, L., Zhang, Y., Sun, J., Liu, X., & Coates, M. (2020, April). Memory augmented graph neural networks for sequential recommendation. In _Proceedings of the AAAI Conference on Artificial Intelligence_ (Vol. 34, No. 04, pp. 5045-5052).](https://ojs.aaai.org/index.php/AAAI/article/download/5945/5801) | AAAI | 2020 | NA |

| STP-UDGAT | [Lim, N., Hooi, B., Ng, S. K., Wang, X., Goh, Y. L., Weng, R., & Varadarajan, J. (2020, October). STP-UDGAT: Spatial-Temporal-Preference User Dimensional Graph Attention Network for Next POI Recommendation. In _Proceedings of the 29th ACM International Conference on Information & Knowledge Management_ (pp. 845-854).](https://arxiv.org/pdf/2010.07024) | CIKM | 2020 | NA |

| GPR | [Chang, B., Jang, G., Kim, S., & Kang, J. (2020, October). Learning graph-based geographical latent representation for point-of-interest recommendation. In _Proceedings of the 29th ACM International Conference on Information & Knowledge Management_ (pp. 135-144).](https://dl.acm.org/doi/abs/10.1145/3340531.3411905) | CIKM | 2020 | NA |

| GES-SASRec | [Zhu, T., Sun, L., & Chen, G. (2021). Graph-based Embedding Smoothing for Sequential Recommendation. _IEEE Transactions on Knowledge and Data Engineering_.](https://ieeexplore.ieee.org/abstract/document/9405450/) | TKDE | 2021 | [Python](https://github.com/zhuty16/GES) |

| RetaGNN | [Hsu, C., & Li, C. T. (2021, April). RetaGNN: Relational Temporal Attentive Graph Neural Networks for Holistic Sequential Recommendation. In _Proceedings of the Web Conference 2021_ (pp. 2968-2979).](https://arxiv.org/pdf/2101.12457) | WWW | 2021 | [Python](https://github.com/retagnn/RetaGNN) |

| TGSRec | [Fan, Z., Liu, Z., Zhang, J., Xiong, Y., Zheng, L., & Yu, P. S. (2021). Continuous-Time Sequential Recommendation with Temporal Graph Collaborative Transformer. _arXiv preprint arXiv:2108.06625_.](https://arxiv.org/pdf/2108.06625) | CIKM | 2021 | [Python](https://github.com/DyGRec/TGSRec) |

| SGRec | [Li, Y., Chen, T., Yin, H., & Huang, Z. (2021). Discovering Collaborative Signals for Next POI Recommendation with Iterative Seq2Graph Augmentation. _arXiv preprint arXiv:2106.15814_.](https://arxiv.org/pdf/2106.15814) | IJCAI | 2021 | NA |

| SURGE | [Chang, J., Gao, C., Zheng, Y., Hui, Y., Niu, Y., Song, Y., ... & Li, Y. (2021, July). Sequential Recommendation with Graph Neural Networks. In _Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval_ (pp. 378-387).](https://arxiv.org/pdf/2106.14226) | SIGIR | 2021 | [Python](https://github.com/tsinghua-fib-lab/SIGIR21-SURGE) |

| GME | [Xie, M., Yin, H., Xu, F., Wang, H., & Zhou, X. (2016, November). Graph-based metric embedding for next poi recommendation. In _International Conference on Web Information Systems Engineering_ (pp. 207-222). Springer, Cham.](http://net.pku.edu.cn/daim/hongzhi.yin/papers/WISE-2016.pdf) | WISE | 2016 | NA |

| Wang _et al._ | [Wang, B., & Cai, W. (2020). Knowledge-enhanced graph neural networks for sequential recommendation. _Information_, _11_(8), 388.](https://www.mdpi.com/2078-2489/11/8/388/pdf) | Information | 2020 | NA |

| DGSR | [Zhang, M., Wu, S., Yu, X., & Wang, L. (2021). Dynamic Graph Neural Networks for Sequential Recommendation. _arXiv preprint arXiv:2104.07368_.](https://arxiv.org/pdf/2104.07368) | arxiv | 2021 | NA |

### Session Recommendation

| **Name** | **Paper** | **Venue** | **Year** | **Code** |

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

| SR-GNN | [Wu, S., Tang, Y., Zhu, Y., Wang, L., Xie, X., & Tan, T. (2019, July). Session-based recommendation with graph neural networks. In _Proceedings of the AAAI Conference on Artificial Intelligence_ (Vol. 33, No. 01, pp. 346-353).](https://ojs.aaai.org/index.php/AAAI/article/view/3804/3682) | AAAI | 2019 | [Python](https://paperswithcode.com/paper/session-based-recommendation-with-graph) |

| GC-SAN | [Xu, C., Zhao, P., Liu, Y., Sheng, V. S., Xu, J., Zhuang, F., ... & Zhou, X. (2019, August). Graph Contextualized Self-Attention Network for Session-based Recommendation. In _IJCAI_ (Vol. 19, pp. 3940-3946).](https://www.ijcai.org/proceedings/2019/0547.pdf) | IJCAI | 2019 | [Python](https://github.com/RUCAIBox/RecBole/blob/master/recbole/model/sequential_recommender/gcsan.py) |

| TA-GNN | [Yu, F., Zhu, Y., Liu, Q., Wu, S., Wang, L., & Tan, T. (2020, July). TAGNN: Target attentive graph neural networks for session-based recommendation. In _Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval_ (pp. 1921-1924).](https://arxiv.org/pdf/2005.02844) | SIGIR | 2020 | [Python](https://github.com/CRIPAC-DIG/TAGNN) |

| MGNN-SPred | [Wang, W., Zhang, W., Liu, S., Liu, Q., Zhang, B., Lin, L., & Zha, H. (2020, April). Beyond clicks: Modeling multi-relational item graph for session-based target behavior prediction. In _Proceedings of The Web Conference 2020_ (pp. 3056-3062).](https://arxiv.org/pdf/2002.07993) | WWW | 2020 | [Python](https://github.com/Autumn945/MGNN-SPred) |

| LESSR | [Chen, T., & Wong, R. C. W. (2020, August). Handling information loss of graph neural networks for session-based recommendation. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (pp. 1172-1180).](http://home.cse.ust.hk/~raywong/paper/kdd20-informationLoss-GNN.pdf) | KDD | 2020 | [Python](https://github.com/twchen/lessr) |

| MKM-SR | [Meng, W., Yang, D., & Xiao, Y. (2020, July). Incorporating user micro-behaviors and item knowledge into multi-task learning for session-based recommendation. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 1091-1100).](https://arxiv.org/pdf/2006.06922) | SIGIR | 2020 | [Python](https://github.com/ciecus/MKM-SR) |

| GAG | [Qiu, R., Yin, H., Huang, Z., & Chen, T. (2020, July). Gag: Global attributed graph neural network for streaming session-based recommendation. In _Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval_ (pp. 669-678).](https://arxiv.org/pdf/2007.02747) | SIGIR | 2020 | [Python](https://github.com/RuihongQiu/GAG) |

| GCE-GNN | [Wang, Z., Wei, W., Cong, G., Li, X. L., Mao, X. L., & Qiu, M. (2020, July). Global context enhanced graph neural networks for session-based recommendation. In _Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval_ (pp. 169-178).](https://arxiv.org/pdf/2106.05081) | SIGIR | 2020 | [Python](https://github.com/CCIIPLab/GCE-GNN) |

| SGNN-HN | [Pan, Z., Cai, F., Chen, W., Chen, H., & de Rijke, M. (2020, October). Star graph neural networks for session-based recommendation. In _Proceedings of the 29th ACM International Conference on Information & Knowledge Management_ (pp. 1195-1204).](https://irlab.science.uva.nl/wp-content/papercite-data/pdf/pan-2020-star.pdf) | CIKM | 2020 | NA |

| DHCN | [Xia, X., Yin, H., Yu, J., Wang, Q., Cui, L., & Zhang, X. (2020). Self-supervised hypergraph convolutional networks for session-based recommendation. _arXiv preprint arXiv:2012.06852_.](https://ojs.aaai.org/index.php/AAAI/article/view/16578) | AAAI | 2021 | [Python](https://github.com/xiaxin1998/DHCN) |

| SHARE | [Wang, J., Ding, K., Zhu, Z., & Caverlee, J. (2021). Session-based Recommendation with Hypergraph Attention Networks. In _Proceedings of the 2021 SIAM International Conference on Data Mining (SDM)_ (pp. 82-90). Society for Industrial and Applied Mathematics.](https://epubs.siam.org/doi/pdf/10.1137/1.9781611976700.10) | SDM | 2021 | NA |

| SERec | [Chen, T., & Wong, R. C. W. (2021, March). An Efficient and Effective Framework for Session-based Social Recommendation. In Proceedings of the 14th ACM International Conference on Web Search and Data Mining (pp. 400-408).](http://www.cse.ust.hk/~raywong/paper/wsdm21-SEFrame.pdf) | WSDM | 2021 | [Python](https://github.com/twchen/SEFrame) |

| COTREC | [Xia, X., Yin, H., Yu, J., Shao, Y., & Cui, L. (2021). Self-Supervised Graph Co-Training for Session-based Recommendation. arXiv preprint arXiv:2108.10560.](https://arxiv.org/pdf/2108.10560) | CIKM | 2021 | [Python](https://github.com/xiaxin1998/COTREC) |

| DAT-MDI | [Chen, C., Guo, J., & Song, B. (2021, July). Dual Attention Transfer in Session-based Recommendation with Multi-dimensional Integration. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 869-878).](https://dl.acm.org/doi/abs/10.1145/3404835.3462866) | SIGIR | 2021 | NA |

| TASRec | [Zhou, H., Tan, Q., Huang, X., Zhou, K., & Wang, X. (2021). Temporal Augmented Graph Neural Networks for Session-Based Recommendations.](https://www4.comp.polyu.edu.hk/~xiaohuang/docs/Huachi_sigir2021.pdf) | SIGIR | 2021 | NA |

| G³SR | [Deng, Z. H., Wang, C. D., Huang, L., Lai, J. H., & Philip, S. Y. (2022). G^ 3SR: Global Graph Guided Session-Based Recommendation. IEEE Transactions on Neural Networks and Learning Systems.](https://arxiv.org/pdf/2203.06467.pdf) | TNNLS | 2022 | NA |

| HG-GNN | [Pang, Y., Wu, L., Shen, Q., Zhang, Y., Wei, Z., Xu, F., ... & Pei, J. (2022, February). Heterogeneous global graph neural networks for personalized session-based recommendation. In Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining (pp. 775-783).](https://arxiv.org/pdf/2107.03813.pdf) | WSDM | 2022 | [Python](https://github.com/0215Arthur/HG-GNN) |

| CGL | [Pan, Z., Cai, F., Chen, W., Chen, C., & Chen, H. (2022). Collaborative Graph Learning for Session-based Recommendation. ACM Transactions on Information Systems (TOIS), 40(4), 1-26.](https://dl.acm.org/doi/10.1145/3490479) | TOIS | 2022 | NA |

| CAGE | [Sheu, H. S., & Li, S. (2020, September). Context-aware graph embedding for session-based news recommendation. In Fourteenth ACM conference on recommender systems (pp. 657-662).](https://dl.acm.org/doi/abs/10.1145/3383313.3418477) | RecSys | 2020 | NA |

| A-PGNN | [Zhang, M., Wu, S., Gao, M., Jiang, X., Xu, K., & Wang, L. (2020). Personalized graph neural networks with attention mechanism for session-aware recommendation. IEEE Transactions on Knowledge and Data Engineering.](https://arxiv.org/pdf/1910.08887) | TKDE | 2020 | [Python](https://paperswithcode.com/paper/personalizing-graph-neural-networks-with) |

| DGTN | [Zheng, Y., Liu, S., Li, Z., & Wu, S. (2020, November). DGTN: Dual-channel Graph Transition Network for Session-based Recommendation. In _2020 International Conference on Data Mining Workshops (ICDMW)_ (pp. 236-242). IEEE.](https://arxiv.org/pdf/2009.10002) | ICDMW | 2020 | [Python](https://github.com/kunwuz/DGTN) |

| FGNN | [Qiu, R., Li, J., Huang, Z., & Yin, H. (2019, November). Rethinking the item order in session-based recommendation with graph neural networks. In _Proceedings of the 28th ACM International Conference on Information and Knowledge Management_ (pp. 579-588).](https://arxiv.org/pdf/1911.11942) | CIKM | 2019 | [Python](https://github.com/RuihongQiu/FGNN) |

### Bundle Recommendation

| **Name** | **Paper** | **Venue** | **Year** | **Code** |

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

| BGCN | [Chang, J., Gao, C., He, X., Jin, D., & Li, Y. (2020, July). Bundle recommendation with graph convolutional networks. In _Proceedings of the 43rd international ACM SIGIR conference on Research and development in Information Retrieval_ (pp. 1673-1676).](https://arxiv.org/pdf/2005.03475) | SIGIR | 2020 | [Python](https://github.com/cjx0525/BGCN) |

| HFGN | [Li, X., Wang, X., He, X., Chen, L., Xiao, J., & Chua, T. S. (2020, July). Hierarchical fashion graph network for personalized outfit recommendation. In _Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval_ (pp. 159-168).](https://arxiv.org/pdf/2005.12566) | SIGIR | 2020 | [Python](https://github.com/xcppy/hierarchical_fashion_graph_network) |

| BundleNet | [Deng, Q., Wang, K., Zhao, M., Zou, Z., Wu, R., Tao, J., ... & Chen, L. (2020, October). Personalized Bundle Recommendation in Online Games. In _Proceedings of the 29th ACM International Conference on Information & Knowledge Management_ (pp. 2381-2388).](https://arxiv.org/pdf/2104.05307) | CIKM | 2020 | NA |

| DPR | [Zheng, Z., Wang, C., Xu, T., Shen, D., Qin, P., Huai, B., ... & Chen, E. (2021, April). Drug Package Recommendation via Interaction-aware Graph Induction. In _Proceedings of the Web Conference 2021_ (pp. 1284-1295).](https://arxiv.org/pdf/2102.03577) | WWW | 2021 | NA |

| DPG | [Zheng, Z., Wang, C., Xu, T., Shen, D., Qin, P., Zhao, X., ... & Chen, E. (2022). Interaction-aware Drug Package Recommendation via Policy Gradient. ACM Transactions on Information Systems (TOIS).](https://dl.acm.org/doi/10.1145/3511020) | TOIS | 2022 | NA |

| MIDGN | [Zhao, S., Wei, W., Zou, D., & Mao, X. (2022). Multi-view intent disentangle graph networks for bundle recommendation. arXiv preprint arXiv:2202.11425.](https://ojs.aaai.org/index.php/AAAI/article/view/20359) | AAAI | 2022 | [Python](https://github.com/Snnzhao/MIDGN) |

### Cross Domain Recommendation

| **Name** | **Paper** | **Venue** | **Year** | **Code** |

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

| PPGN | [Zhao, C., Li, C., & Fu, C. (2019, November). Cross-domain recommendation via preference propagation graphnet. In _Proceedings of the 28th ACM International Conference on Information and Knowledge Management_ (pp. 2165-2168).](https://dl.acm.org/doi/abs/10.1145/3357384.3358166) | CIKM | 2019 | [Python](https://github.com/WHUIR/PPGN) |

| BiTGCF | [Liu, M., Li, J., Li, G., & Pan, P. (2020, October). Cross Domain Recommendation via Bi-directional Transfer Graph Collaborative Filtering Networks. In _Proceedings of the 29th ACM International Conference on Information & Knowledge Management_ (pp. 885-894).](https://dl.acm.org/doi/abs/10.1145/3340531.3412012) | CIKM | 2020 | [Python](https://github.com/sunshinelium/Bi-TGCF) |

| DAN | [Wang, B., Zhang, C., Zhang, H., Lyu, X., & Tang, Z. (2020, October). Dual Autoencoder Network with Swap Reconstruction for Cold-Start Recommendation. In _Proceedings of the 29th ACM International Conference on Information & Knowledge Management_ (pp. 2249-2252).](https://dl.acm.org/doi/abs/10.1145/3340531.3412069) | CIKM | 2020 | NA |

| HeroGRAPH | [Cui, Q., Wei, T., Zhang, Y., & Zhang, Q. (2020). HeroGRAPH: A Heterogeneous Graph Framework for Multi-Target Cross-Domain Recommendation. In _ORSUM@ RecSys_.](http://ceur-ws.org/Vol-2715/paper6.pdf) | RecSys | 2020 | [Python](https://github.com/cuiqiang1990/HeroGRAPH) |

| DAGCN | [Guo, L., Tang, L., Chen, T., Zhu, L., Nguyen, Q. V. H., & Yin, H. (2021). DA-GCN: A Domain-aware Attentive Graph Convolution Network for Shared-account Cross-domain Sequential Recommendation. _arXiv preprint arXiv:2105.03300_.](https://arxiv.org/pdf/2105.03300) | IJCAI | 2021 | NA |

## Recommendation Objectives

### Multi-behavior Recommendation

| **Name** | **Paper** | **Venue** | **Year** | **Code** |

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

| MBGCN | [Jin, B., Gao, C., He, X., Jin, D., & Li, Y. (2020, July). Multi-behavior recommendation with graph convolutional networks. In _Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval_ (pp. 659-668).](http://staff.ustc.edu.cn/~hexn/papers/sigir20-MBGCN.pdf) | SIGIR | 2020 | [Python](https://github.com/tsinghua-fib-lab/MBGCN) |

| MGNN-SPred | [Wang, W., Zhang, W., Liu, S., Liu, Q., Zhang, B., Lin, L., & Zha, H. (2020, April). Beyond clicks: Modeling multi-relational item graph for session-based target behavior prediction. In _Proceedings of The Web Conference 2020_ (pp. 3056-3062).](https://arxiv.org/pdf/2002.07993) | WWW | 2020 | [Python](https://github.com/Autumn945/MGNN-SPred) |

| MGNN | [Zhang, W., Mao, J., Cao, Y., & Xu, C. (2020, October). Multiplex Graph Neural Networks for Multi-behavior Recommendation. In _Proceedings of the 29th ACM International Conference on Information & Knowledge Management_ (pp. 2313-2316).](https://dl.acm.org/doi/abs/10.1145/3340531.3412119) | CIKM | 2020 | NA |

| LP-MRGNN | [Wang, W., Zhang, W., Liu, S., Liu, Q., Zhang, B., Lin, L., & Zha, H. (2021). Incorporating Link Prediction into Multi-Relational Item Graph Modeling for Session-based Recommendation. _IEEE Transactions on Knowledge and Data Engineering_.](https://ieeexplore.ieee.org/abstract/document/9536374/) | TKDE | 2021 | NA |

| GNMR | [Xia, L., Huang, C., Xu, Y., Dai, P., Lu, M., & Bo, L. (2021, April). Multi-Behavior Enhanced Recommendation with Cross-Interaction Collaborative Relation Modeling. In _2021 IEEE 37th International Conference on Data Engineering (ICDE)_ (pp. 1931-1936). IEEE.](https://ieeexplore.ieee.org/abstract/document/9458929) | ICDE | 2021 | [Python](https://github.com/akaxlh/GNMR) |

| MB-GMN | [Xia, L., Xu, Y., Huang, C., Dai, P., & Bo, L. (2021, July). Graph meta network for multi-behavior recommendation. In _Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval_ (pp. 757-766).](https://dl.acm.org/doi/abs/10.1145/3404835.3462972) | SIGIR | 2021 | [Python](https://github.com/akaxlh/MB-GMN) |

| KHGT | [Xia, L., Huang, C., Xu, Y., Dai, P., Zhang, X., Yang, H., ... & Bo, L. (2021, May). Knowledge-Enhanced Hierarchical Graph Transformer Network for Multi-Behavior Recommendation. In _Proceedings of the AAAI Conference on Artificial Intelligence_ (Vol. 35, No. 5, pp. 4486-4493).](https://www.aaai.org/AAAI21Papers/AAAI-3071.XiaL.pdf) | AAAI | 2021 | [Python](https://github.com/akaxlh/KHGT) |

| GHCF | [Chen, C., Ma, W., Zhang, M., Wang, Z., He, X., Wang, C., ... & Ma, S. (2021, May). Graph Heterogeneous Multi-Relational Recommendation. In _Proceedings of the AAAI Conference on Artificial Intelligence_ (Vol. 35, No. 5, pp. 3958-3966).](https://www.aaai.org/AAAI21Papers/AAAI-615.ChenC.pdf) | AAAI | 2021 | [Python](https://github.com/chenchongthu/GHCF) |

| DMBGN | [Xiao, F., Li, L., Xu, W., Zhao, J., Yang, X., Lang, J., & Wang, H. (2021). DMBGN: Deep Multi-Behavior Graph Networks for Voucher Redemption Rate Prediction. _arXiv preprint arXiv:2106.03356_.](https://arxiv.org/pdf/2106.03356) | KDD | 2021 | [Python](https://github.com/fengtong-xiao/DMBGN) |

| HMG-CR | [Yang, H., Chen, H., Li, L., Yu, P. S., & Xu, G. (2021). Hyper Meta-Path Contrastive Learning for Multi-Behavior Recommendation. _arXiv preprint arXiv:2109.02859_.](https://arxiv.org/pdf/2109.02859) | ICDM | 2021 | [Python](https://github.com/Haoran-Young/HMG-CR) |

| GNNH | [Yu, B., Zhang, R., Chen, W., & Fang, J. (2021). Graph neural network based model for multi-behavior session-based recommendation. _GeoInformatica_, 1-19.](https://link.springer.com/article/10.1007/s10707-021-00439-w) | GeoInformatica | 2021 | NA |

### Diversity

| **Name** | **Paper** | **Venue** | **Year** | **Code** |

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

| V2HT | [Li, M., Gan, T., Liu, M., Cheng, Z., Yin, J., & Nie, L. (2019, November). Long-tail hashtag recommendation for micro-videos with graph convolutional network. In _Proceedings of the 28th ACM International Conference on Information and Knowledge Management_ (pp. 509-518).](https://dl.acm.org/doi/abs/10.1145/3357384.3357912) | CIKM | 2019 | NA |

| BGCF | [Sun, J., Guo, W., Zhang, D., Zhang, Y., Regol, F., Hu, Y., ... & Coates, M. (2020, August). A framework for recommending accurate and diverse items using bayesian graph convolutional neural networks. In _Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining_ (pp. 2030-2039).](https://www.researchgate.net/profile/Jianing-Sun-5/publication/343780326_A_Framework_for_Recommending_Accurate_and_Diverse_Items_Using_Bayesian_Graph_Convolutional_Neural_Networks/links/5f85d507299bf1b53e23724f/A-Framework-for-Recommending-Accurate-and-Diverse-Items-Using-Bayesian-Graph-Convolutional-Neural-Networks.pdf) | KDD | 2020 | [Python](https://gitee.com/mindspore/models/tree/master/official/gnn/bgcf) |

| DGCN | [Zheng, Y., Gao, C., Chen, L., Jin, D., & Li, Y. (2021, April). DGCN: Diversified Recommendation with Graph Convolutional Networks. In _Proceedings of the Web Conference 2021_ (pp. 401-412).](https://arxiv.org/pdf/2108.06952.pdf) | WWW | 2021 | [Python](https://github.com/tsinghua-fib-lab/DGCN) |

| FH-HAT | [Xie, R., Liu, Q., Liu, S., Zhang, Z., Cui, P., Zhang, B., & Lin, L. (2021). Improving Accuracy and Diversity in Matching of Recommendation with Diversified Preference Network. arXiv preprint arXiv:2102.03787.](https://arxiv.org/pdf/2102.03787) | TBD | 2021 | NA |

| Isufi _et al._ | [Isufi, E., Pocchiari, M., & Hanjalic, A. (2021). Accuracy-diversity trade-off in recommender systems via graph convolutions. _Information Processing & Management_, _58_(2), 102459.](https://www.sciencedirect.com/science/article/pii/S0306457320309511) | IPM | 2021 | [Python](https://github.com/esilezz/accdiv-via-graphconv) |

### Explainability

| **Name** | **Paper** | **Venue** | **Year** | **Code** |

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

| RippleNet | [Wang, H., Zhang, F., Wang, J., Zhao, M., Li, W., Xie, X., & Guo, M. (2018, October). Ripplenet: Propagating user preferences on the knowledge graph for recommender systems. In _Proceedings of the 27th ACM International Conference on Information and Knowledge Management_ (pp. 417-426).](https://arxiv.org/pdf/1803.03467) | CIKM | 2018 | [Python](https://paperswithcode.com/paper/ripplenet-propagating-user-preferences-on-the) |

| EIUM | [Huang, X., Fang, Q., Qian, S., Sang, J., Li, Y., & Xu, C. (2019, October). Explainable interaction-driven user modeling over knowledge graph for sequential recommendation. In _Proceedings of the 27th ACM International Conference on Multimedia_ (pp. 548-556).](https://dl.acm.org/doi/abs/10.1145/3343031.3350893) | MM | 2019 | NA |

| KPRN | [Wang, X., Wang, D., Xu, C., He, X., Cao, Y., & Chua, T. S. (2019, July). Explainable reasoning over knowledge graphs for recommendation. In _Proceedings of the AAAI Conference on Artificial Intelligence_ (Vol. 33, No. 01, pp. 5329-5336).](https://ojs.aaai.org/index.php/AAAI/article/view/4470/4348) | AAAI | 2019 | [Python](https://paperswithcode.com/paper/explainable-reasoning-over-knowledge-graphs) |

| RuleRec | [Ma, W., Zhang, M., Cao, Y., Jin, W., Wang, C., Liu, Y., ... & Ren, X. (2019, May). Jointly learning explainable rules for recommendation with knowledge graph. In _The World Wide Web Conference_ (pp. 1210-1221).](https://arxiv.org/pdf/1903.03714) | WWW | 2019 | [Python](https://github.com/THUIR/RuleRec) |

| PGPR | [Xian, Y., Fu, Z., Muthukrishnan, S., De Melo, G., & Zhang, Y. (2019, July). Reinforcement knowledge graph reasoning for explainable recommendation. In _Proceedings of the 42nd international ACM SIGIR conference on research and development in information retrieval_ (pp. 285-294).](https://arxiv.org/pdf/1906.05237) | SIGIR | 2019 | [Python](https://github.com/orcax/PGPR) |

| KGAT | [Wang, X., He, X., Cao, Y., Liu, M., & Chua, T. S. (2019, July). Kgat: Knowledge graph attention network for recommendation. In _Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining_ (pp. 950-958).](https://arxiv.org/pdf/1905.07854) | KDD | 2019 | [Python](https://paperswithcode.com/paper/kgat-knowledge-graph-attention-network-for) |

| TMER | [Chen, H., Li, Y., Sun, X., Xu, G., & Yin, H. (2021, March). Temporal meta-path guided explainable recommendation. In _Proceedings of the 14th ACM International Conference on Web Search and Data Mining_ (pp. 1056-1064).](https://arxiv.org/pdf/2101.01433) | WSDM | 2021 | [Python](https://github.com/Abigale001/TMER) |

| ECFKG | [Bose, A., & Hamilton, W. (2019, May). Compositional fairness constraints for graph embeddings. In _International Conference on Machine Learning_ (pp. 715-724). PMLR.](http://proceedings.mlr.press/v97/bose19a/bose19a.pdf) | ICML | 2019 | [Python](https://github.com/joeybose/Flexible-Fairness-Constraints) |

| HAGERec | [Yang, Z., & Dong, S. (2020). HAGERec: hierarchical attention graph convolutional network incorporating knowledge graph for explainable recommendation. _Knowledge-Based Systems_, _204_, 106194.](https://www.sciencedirect.com/science/article/pii/S0950705120304196) | KBS | 2020 | NA |

### Fairness

| **Name** | **Paper** | **Venue** | **Year** | **Code** |

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

| FairGo | [Wu, L., Chen, L., Shao, P., Hong, R., Wang, X., & Wang, M. (2021, April). Learning Fair Representations for Recommendation: A Graph-based Perspective. In _Proceedings of the Web Conference 2021_ (pp. 2198-2208).](https://arxiv.org/pdf/2102.09140) | WWW | 2021 | [Python](https://github.com/newlei/FairGo) |

| FairGNN | [Dai, E., & Wang, S. (2021, March). Say no to the discrimination: Learning fair graph neural networks with limited sensitive attribute information. In _Proceedings of the 14th ACM International Conference on Web Search and Data Mining_ (pp. 680-688).](https://arxiv.org/pdf/2009.01454) | WSDM | 2021 | [Python](https://github.com/EnyanDai/FairGNN) |

| Fairwalk | [Rahman, T., Surma, B., Backes, M., & Zhang, Y. (2019). Fairwalk: Towards fair graph embedding.](https://publications.cispa.saarland/2933/1/IJCAI19.pdf) | IJCAI | 2019 | [Python](https://paperswithcode.com/paper/fairwalk-towards-fair-graph-embedding) |

| CFCGE | [Bose, A., & Hamilton, W. (2019, May). Compositional fairness constraints for graph embeddings. In _International Conference on Machine Learning_ (pp. 715-724). PMLR.](http://proceedings.mlr.press/v97/bose19a/bose19a.pdf) | ICML | 2019 | [Python](https://github.com/joeybose/Flexible-Fairness-Constraints) |