└── README.md


/README.md:
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  1 | <p align="center">
  2 |  <img width="100px" src="https://res.cloudinary.com/anuraghazra/image/upload/v1594908242/logo_ccswme.svg" align="center" alt="Awesome GNN Research" />
  3 |  <h2 align="center">Awesome GNN Research</h2>
  4 | </p>
  5 | 
  6 |   <div align=center>  
  7 |       <img src="https://github-readme-stats.vercel.app/api?username=XunkaiLi&show_icons=true&theme=gruvbox_light"/>
  8 |   </div>
  9 | 
 10 | :fire: **Top AI&ML&DM Conferences or Journals and arXiv Prep.**
 11 | 
 12 | CVPR, ICCV, ECCV, ACL, MM, etc
 13 | 
 14 | AAAI, IJCAI, SIGIR, ICDE, SIGMOD, etc
 15 | 
 16 | KDD, WWW, ICLR, ICML, NeurIPS, TKDE, etc
 17 | 
 18 | :star: **Research Keywords**
 19 | 
 20 | Scalable Graph Neural Networks, Federated Graph Learning, Recommender system Based on GNN
 21 | 
 22 | # Wizardship
 23 | 
 24 | - [Scalable Graph Neural Networks](#Scalable-Graph-Neural-Networks)
 25 |   - [Graph Embedding](#Graph-Embedding)
 26 |   - [Linear-model based Graph Neural Networks](#Linear-model-based-Graph-Neural-Networks)
 27 |   - [Sampling based Graph Neural Networks](#Sampling-based-Graph-Neural-Networks)
 28 |   - [Model Compression and Quantification](#Model-Compression-and-Quantification)
 29 |   - [Efficient Architecture and Paradigm](#Efficient-Architecture-and-Paradigm)
 30 |   - [Graph Data Augmentation](#Graph-Data-Augmentation)
 31 |   - [Imbalance Graph Neural Networks](#Imbalance-Graph-Neural-Networks)
 32 | - [Federated Graph Learning](#Federated-Graph-Learning)
 33 |   - [Personalized and Heterogeneous Federated Learning in CV or NLP](#Personalized-and-Heterogeneous-Federated-Learning-in-CV-or-NLP)
 34 |   - [Theoretical Analysis of Federated Learning in CV or NLP](#Theoretical-Analysis-of-Federated-Learning-in-CV-or-NLP)
 35 |   - [CV or NLP Model Compression and Quantification in Federated Learning ](#CV-or-NLP-Model-Compression-and-Quantification-in-Federated-Learning )
 36 |   - [Transfer Federated Graph Learning and Graph Structure Federated Learning](#Transfer-Federated-Graph-Learning-and-Graph-Structure-Federated-Learning)
 37 |   - [Intra-Graph Horizontal Federated Learning](#Intra-Graph-Horizontal-Federated-Learning)
 38 |   - [Inter-Graph Horizontal Federated Learning](#Inter-Graph-Horizontal-Federated-Learning)
 39 |   - [Vertical Federal Learning](#Vertical-Federal-Learning)
 40 | - [Privacy Graph Neural Networks](#Privacy-Graph-Neural-Networks)
 41 | - [Survey and Framework Toolkits](#Survey-and-Framework-Toolkits)
 42 | 
 43 | # Scalable Graph Neural Networks
 44 | 
 45 | ## Graph Embedding
 46 | 
 47 | - KDD'14 DeepWalk: Online Learning of Social Representations [[Paper](https://arxiv.org/abs/1403.6652)] [[Code](https://github.com/phanein/deepwalk)] [[Link](https://zhuanlan.zhihu.com/p/412713441)]
 48 | - WWW'15 LINE: Large-scale Information Network Embedding [[Paper](https://arxiv.org/abs/1503.03578)] [[Code](https://github.com/snowkylin/line)] [[Link](https://zhuanlan.zhihu.com/p/412787557)]
 49 | - KDD'16 node2vec: Scalable Feature Learning for Networks [[Paper](https://arxiv.org/abs/1607.00653)] [[Code](https://github.com/eliorc/node2vec)] [[Link](https://zhuanlan.zhihu.com/p/413046898)]
 50 | - NeurIPS'13  Distributed Representations of Words and Phrases and their Compositionality [[Paper](https://arxiv.org/abs/1310.4546)] [[Code](https://github.com/brijml/mikolov_word2vec)] [[Link](https://zhuanlan.zhihu.com/p/413169135)]
 51 | - KDD'16 Structural Deep Network Embedding [[Paper](http://www.kdd.org/kdd2016/papers/files/rfp0191-wangAemb.pdf)] [[Code](https://github.com/suanrong/SDNE)] [[Link](https://zhuanlan.zhihu.com/p/413468532)]
 52 | 
 53 | ## Linear-model based Graph Neural Networks
 54 | 
 55 | - ICML'19 Simplifying Graph Convolutional Networks [[Paper](https://arxiv.org/abs/1902.07153v1)] [[Code](https://github.com/Tiiiger/SGC)] [[Link](https://zhuanlan.zhihu.com/p/411236675)]
 56 | - ICLR'19 Predict Then Propagate: Graph Neural Networks Meet Personalized PageRank [[Paper](https://arxiv.org/abs/1810.05997v5)] [[Code](https://github.com/benedekrozemberczki/APPNP)] [[Link](https://zhuanlan.zhihu.com/p/419843669)]
 57 | - arXiv'20 Scalable Graph Neural Networks for Heterogeneous Graphs [[Paper](https://arxiv.org/abs/2011.09679)] [[Code](https://github.com/facebookresearch/NARS)] [[Link](https://zhuanlan.zhihu.com/p/490723967)]
 58 | - arXiv'20 Unifying Graph Convolutional Neural Networks and Label Propagation [[Paper](https://arxiv.org/abs/2002.06755)] [[Code](https://github.com/liu6zijian/GCN-LPA-PyTorch)] [[Link](https://zhuanlan.zhihu.com/p/501120937)]
 59 | - NeurIPS‘21 Node Dependent Local Smoothing for Scalable Graph Learning [[Paper](https://arxiv.org/abs/2110.14377)] [[Code](https://github.com/zwt233/NDLS)] [[Link](https://zhuanlan.zhihu.com/p/445986238)]
 60 | - arXiv'21 Scalable and Adaptive Graph Neural Networks with Self-Label-Enhanced Training [[Paper](https://arxiv.org/pdf/2104.09376.pdf)] [[Code](https://github.com/skepsun/SAGN_with_SLE)] [[Link](https://zhuanlan.zhihu.com/p/543120470)]
 61 | - ICLR'21 Combining Label Propagation and Simple Models Out-performs Graph Neural Networks [[Paper](https://arxiv.org/pdf/2010.13993.pdf)] [[Code](https://github.com/CUAI/CorrectAndSmooth)] [[Link](https://zhuanlan.zhihu.com/p/543120470)]
 62 | - ICLR'22 Node Feature Extraction by Self-Supervised Multi-scale Neighborhood Prediction [[Paper](https://arxiv.org/pdf/2111.00064.pdf)] [[Code](https://github.com/elichienxD/SAGN_with_SLE)] [[Link](https://zhuanlan.zhihu.com/p/543120470)]
 63 | - arXiv'22 SCR: Training Graph Neural Networks with Consistency Regularization [[Paper](https://arxiv.org/pdf/2112.04319.pdf)] [[Code](https://github.com/THUDM/SCR)] [[Link](https://zhuanlan.zhihu.com/p/543120470)]
 64 | - KDD'22 Graph Attention MLP with Reliable Label Utilization [[Paper](https://arxiv.org/pdf/2108.10097.pdf)] [[Code](https://github.com/PKU-DAIR/GAMLP)] [[Link](https://zhuanlan.zhihu.com/p/543120470)]
 65 | 
 66 | ## Sampling based Graph Neural Networks
 67 | 
 68 | - NeurIPS'17 Inductive Representation Learning on Large Graphs [[Paper](https://arxiv.org/abs/1706.02216v2)] [[Code](https://github.com/williamleif/GraphSAGE)] [[Link](https://zhuanlan.zhihu.com/p/411612848)]
 69 | - ICLR'18 FASTGCN: Fast Learning With Graph Convolutional Networks Via Importance Sampling [[Paper](https://arxiv.org/abs/1801.10247)] [[Code](https://github.com/matenure/FastGCN)] [[Link](https://zhuanlan.zhihu.com/p/412020874)]
 70 | 
 71 | ## Model Compression and Quantification
 72 | 
 73 | - NeurIPS'14 Distilling the Knowledge in a Neural Network [[Paper](https://arxiv.org/pdf/1503.02531.pdf)] [[Code](https://github.com/JoonyoungYi/KD-pytorch)] [[Link](https://zhuanlan.zhihu.com/p/542550895)]
 74 | - ICLR'15 FitNets: Hints for Thin Deep Nets [[Paper](https://arxiv.org/pdf/1412.6550.pdf)] [No Code] [[Link](https://zhuanlan.zhihu.com/p/542550895)]
 75 | - ICLR'17 Paying More Attention to Attention: Improving The Performance of Convolutional Neural Networks via Attention Transfer [[Paper](https://arxiv.org/pdf/1612.03928.pdf)] [[Code](https://github.com/szagoruyko/attention-transfer)] [[Link](https://zhuanlan.zhihu.com/p/542550895)]
 76 | - ICCV'19 Similarity-Preserving Knowledge Distillation [[Paper](https://arxiv.org/pdf/1907.09682.pdf)] [No Code] [[Link](https://zhuanlan.zhihu.com/p/542398176)]
 77 | - ICCV'19  Be Your Own Teacher: Improve the Performance of Convolutional Neural Networks via Self Distillation [[Paper](https://openaccess.thecvf.com/content_ICCV_2019/papers/Zhang_Be_Your_Own_Teacher_Improve_the_Performance_of_Convolutional_Neural_ICCV_2019_paper.pdf)] [[Code](https://github.com/luanyunteng/pytorch-be-your-own-teacher)] [[Link](https://zhuanlan.zhihu.com/p/545136524)]
 78 | - CVPR'21 Distilling Knowledge via Knowledge Review  [[Paper](https://arxiv.org/pdf/2104.09044.pdf)] [[Code](https://github.com/dvlab-research/ReviewKD)] [[Link](https://zhuanlan.zhihu.com/p/540706096)]
 79 | - CVPR'21 Distill on the Go: Online knowledge distillation in self-supervised learning [[Paper](https://openaccess.thecvf.com/content/CVPR2021W/LLID/papers/Bhat_Distill_on_the_Go_Online_Knowledge_Distillation_in_Self-Supervised_Learning_CVPRW_2021_paper.pdf)] [[Code](https://github.com/NeurAI-Lab/DoGo)] [[Link](https://zhuanlan.zhihu.com/p/545141466)]
 80 | - CVPR'22 Decoupled Knowledge Distillation [[Paper](https://arxiv.org/pdf/2203.08679.pdf)] [[Code](https://github.com/megvii-research/mdistiller)] [[Link](https://zhuanlan.zhihu.com/p/540345474)]
 81 | 
 82 | ------
 83 | 
 84 | 
 85 | 
 86 | - CVPR'20 Distilling Knowledge from Graph Convolutional Networks [[Paper](https://arxiv.org/abs/2003.10477)] [[Code](https://github.com/ihollywhy/DistillGCN.PyTorch)] [[Link](https://zhuanlan.zhihu.com/p/522926912)]
 87 | - SIGMOD'20 Reliable Data Distillation on Graph Convolutional Network [[Paper](http://olivier.ruas.free.fr/papers/SIGMOD20.pdf)] [No Code] [[Link](https://zhuanlan.zhihu.com/p/524706697)]
 88 | - KDD'20 TinyGNN: Learning Efficient Graph Neural Networks [[Paper](https://www.kdd.org/kdd2020/accepted-papers/view/tinygnn-learning-efficient-graph-neural-networks)] [No Code] [[Link](https://zhuanlan.zhihu.com/p/534364243)] 
 89 | - arXiv’21 Distilling Self-Knowledge From Contrastive Links to Classify Graph Nodes Without Passing Messages [[Paper](https://arxiv.org/pdf/2106.08541.pdf)] [[Code](https://github.com/cf020031308/LinkDist)] [[Link](https://zhuanlan.zhihu.com/p/528128661)]
 90 | - MICCAI'21 GKD: Semi-supervised Graph Knowledge Distillation for Graph-Independent Inference [[Paper](https://arxiv.org/pdf/2104.03597.pdf)] [[Code](https://github.com/mahsa91/GKD)] [[Link](https://zhuanlan.zhihu.com/p/529988634)]
 91 | - CVPR'21 Bi-GCN: Binary Graph Convolutional Network [[Paper](https://arxiv.org/abs/2010.07565)] [[Code](https://github.com/bywmm/Bi-GCN)] [[Link](https://zhuanlan.zhihu.com/p/520825504)]
 92 | - IJCAI'21 Graph-Free Knowledge Distillation for Graph Neural Networks [[Paper](https://www.ijcai.org/proceedings/2021/0320.pdf)] [[Code](https://github.com/Xiang-Deng-DL/GFKD)] [[Link](https://zhuanlan.zhihu.com/p/526258612)]
 93 | - IJCAI'21 On Self-Distilling Graph Neural Network [[Paper](https://arxiv.org/abs/2011.02255)] [No Code] [[Link](https://zhuanlan.zhihu.com/p/522345993)]
 94 | - ICLR'21 On Graph Neural Networks versus Graph-Augmented MLPs [[Paperr](https://arxiv.org/pdf/2010.15116.pdf)] [[Code](https://github.com/leichen2018/GNN_vs_GAMLP)] [[Link](https://zhuanlan.zhihu.com/p/536284429)]
 95 | - WWW'21  Extract the Knowledge of Graph Neural Networks and Go Beyond it: An Effective Knowledge Distillation Framework  [[Paper](https://arxiv.org/abs/2103.02885)] [[Code](https://github.com/BUPT-GAMMA/CPF)] [[Link](https://zhuanlan.zhihu.com/p/523829567)]
 96 | - KDD'21 ROD: Reception-aware Online Distillation for Sparse Graphs [[Paper](https://arxiv.org/pdf/2107.11789.pdf)] [[Code](https://github.com/zwt233/ROD)] [[Link](https://zhuanlan.zhihu.com/p/524839956)]
 97 | - arXiv'22 On Representation Knowledge Distillation for Graph Neural Networks [[Paper](https://arxiv.org/pdf/2111.04964.pdf)] [No Code] [[Link](https://zhuanlan.zhihu.com/p/529728564)]
 98 | - AAAI'22 Workshop  Scalable Consistency Training for Graph Neural Networks via Self-Ensemble Self-Distillation [[Paper](https://arxiv.org/pdf/2110.06290.pdf)] [No Code] [[Link](https://zhuanlan.zhihu.com/p/529094761)]
 99 | - ICLR'22 Graph-less Neural Networks: Teaching Old MLPs New Tricks via Distillation [[Paper](https://arxiv.org/abs/2110.08727)] [[Code](https://github.com/snap-research/graphless-neural-networks)] [[Link](https://zhuanlan.zhihu.com/p/523383588)]
100 | - KDD'22 Compressing Deep Graph Neural Networks via Adversarial Knowledge Distillation [[Paper](https://arxiv.org/pdf/2205.11678.pdf)] [[Code](https://github.com/MIRALab-USTC/GraphAKD)] [[Link](https://zhuanlan.zhihu.com/p/525223627)]
101 | - ICLR'22 Cold Brew Distilling Graph Node Representations with Incomplete or Missing Neighborhoods [[Paper](https://arxiv.org/abs/2111.04840)] [[Code](https://github.com/amazon-research/gnn-tail-generalization)] [[Link](https://zhuanlan.zhihu.com/p/511394613)]
102 | - WSDM'22 Deep Graph-level Anomaly Detection by Glocal Knowledge Distillation [[Paper](https://arxiv.org/pdf/2112.10063.pdf)] [[Code](https://github.com/RongrongMa/GLocalKD)] [[Link](https://zhuanlan.zhihu.com/p/542836644)]
103 | 
104 | ## Efficient Architecture and Paradigm
105 | 
106 | - ICLR'19 How Powerful are Graph Neural Networks? [[Paper](https://arxiv.org/abs/1810.00826)] [[Code](https://github.com/weihua916/powerful-gnns)] [[Link](https://zhuanlan.zhihu.com/p/464818620)] 
107 | - NeurIPS'20 Graph Random Neural Networks for Semi-Supervised Learning on Graphs [[Paper](https://arxiv.org/pdf/2005.11079.pdf)] [[Code](https://github.com/THUDM/GRAND)] [[Link](https://zhuanlan.zhihu.com/p/536767558)]
108 | - arXiv'21 Graph Learning with 1D Convolutions on Random Walks [[Paper](https://arxiv.org/abs/2102.08786)] [[Code](https://github.com/toenshoff/CRaWl)] [[Link](https://zhuanlan.zhihu.com/p/434173732)]
109 | - WSDM'21 Node Similarity Preserving Graph Convolutional Networks [[Paper](https://arxiv.org/pdf/2011.09643.pdf)] [[Code](https://github.com/ChandlerBang/SimP-GCN)] [[Link](https://zhuanlan.zhihu.com/p/536794358)] 
110 | - ICLR'22 A New Perspective on "How Graph Neural Networks Go Beyond Weisfeiler-Lehman?" [[Paper](https://openreview.net/forum?id=uxgg9o7bI_3)] [No Code] [[Link](https://zhuanlan.zhihu.com/p/465200994)] 
111 | - KDD'22 Model Degradation Hinders Deep Graph Neural Networks [[Paper](https://arxiv.org/pdf/2206.04361.pdf)] [[Code](https://github.com/zwt233/AIR)] [[Link](https://zhuanlan.zhihu.com/p/538767995)]
112 | - KDD'22 Feature Overcorrelation in Deep Graph Neural Networks: A New Perspective [[Paper](https://arxiv.org/pdf/2206.07743.pdf)] [[Code](https://github.com/ChandlerBang/DeCorr)] [[Link](https://zhuanlan.zhihu.com/p/541796205)]
113 | 
114 | ## Graph Data Augmentation
115 | 
116 | - KDD'20 NodeAug: Semi-Supervised Node Classification with Data Augmentation [[Paper](https://bhooi.github.io/papers/nodeaug_kdd20.pdf)] [No Code] [[Link](https://zhuanlan.zhihu.com/p/466885671)] 
117 | - arXiv'21 Local Augmentation for Graph Neural Networks [[Paper](https://arxiv.org/pdf/2109.03856.pdf)] [No Code] [[Link](https://zhuanlan.zhihu.com/p/467800945)] 
118 | - AAAI'21 Data Augmentation for Graph Neural Networks [[Paper](https://www.aaai.org/AAAI21Papers/AAAI-10012.ZhaoT.pdf)] [[Code](https://github.com/zhao-tong/GAug)] [[Link](https://zhuanlan.zhihu.com/p/466617745)] 
119 | - WWW'21 Graph Contrastive Learning with Adaptive Augmentation [[Paper](https://arxiv.org/abs/2010.14945)] [[Code](https://github.com/CRIPAC-DIG/GCA)] [[Link](https://zhuanlan.zhihu.com/p/446868435)] 
120 | - AAAI'22 Regularizing Graph Neural Networks via Consistency-Diversity Graph Augmentations [[Paper](http://shichuan.org/doc/126.pdf)] [No Code] [[Link](https://zhuanlan.zhihu.com/p/466661027)] 
121 | - AAAI'21 GraphMix: Improved Training of GNNs for Semi-Supervised Learning [[Paper](https://arxiv.org/abs/1909.11715)] [[Code](https://github.com/vikasverma1077/GraphMix)] [[Link](https://zhuanlan.zhihu.com/p/467389235)] 
122 | - CVPR'22 Robust Optimization as Data Augmentation for Large-scale Graphs [[Paper](https://openaccess.thecvf.com/content/CVPR2022/papers/Kong_Robust_Optimization_As_Data_Augmentation_for_Large-Scale_Graphs_CVPR_2022_paper.pdf)] [[Code](https://github.com/devnkong/FLAG)] [[Link](https://zhuanlan.zhihu.com/p/527957286)]
123 | - AAAI'22 SAIL: Self-Augmented Graph Contrastive Learning [[Paper](https://arxiv.org/pdf/2009.00934.pdf)] [No Code] [[Link](https://zhuanlan.zhihu.com/p/538091329)]
124 | 
125 | ## Imbalance Graph Neural Networks
126 | 
127 | - arXiv'20 Non-Local Graph Neural Networks [[Paper](https://arxiv.org/abs/2005.14612v1)] [No Code] [[Link](https://zhuanlan.zhihu.com/p/444884642)] 
128 | - arXiv'20 Non-IID Graph Neural Networks [[Paper](https://arxiv.org/abs/2005.12386)] [No Code] [[Link](https://zhuanlan.zhihu.com/p/444278767)] 
129 | - WSDM'21 GraphSMOTE: Imbalanced Node Classification on Graphs with Graph Neural Networks [[Paper](https://arxiv.org/abs/2103.08826)] [[Code](https://github.com/TianxiangZhao/GraphSmote)] [[Link](https://zhuanlan.zhihu.com/p/445035949)] 
130 | - IJCAI'21 Multi-Class Imbalanced Graph Convolutional Network Learning [[Paper](https://www.ijcai.org/proceedings/2020/0398.pdf)] [[Code](https://github.com/codeshareabc/DRGCN)] [[Link](https://zhuanlan.zhihu.com/p/446314982)]  
131 | - NeurIPS’21 Shift-Robust GNNs: Overcoming the Limitations of Localized Graph Training Data [[Paper](https://link.zhihu.com/?target=https%3A//proceedings.neurips.cc/paper/2021/file/eb55e369affa90f77dd7dc9e2cd33b16-Paper.pdf)] [[Code](https://github.com/GentleZhu/Shift-Robust-GNNs)] [[Link](https://zhuanlan.zhihu.com/p/522066981)]
132 | 
133 | # Federated Graph Learning
134 | 
135 | - Big Data'19 SGNN: A Graph Neural Network Based Federated Learning Approach by Hiding Structure [[Paper](https://ieeexplore.ieee.org/abstract/document/9005983)] [No Code] [[Link](https://zhuanlan.zhihu.com/p/430953193)] 
136 | 
137 | - arXiv'20 Federated Dynamic GNN with Secure Aggregation [[Paper](https://arxiv.org/abs/2009.07351)] [No Code] [[Link](https://zhuanlan.zhihu.com/p/441686576)] 
138 | - arXiv'21 GIST: Distributed Training for Large-Scale Graph Convolutional Networks [[Paper](https://arxiv.org/abs/2102.10424)] [[Code](https://github.com/wolfecameron/GIST)] [[Link](https://zhuanlan.zhihu.com/p/433427134)]
139 | - TSIPN'21 Distributed Training of Graph Convolutional Networks [[Paper](https://arxiv.org/abs/2007.06281)] [No Code] [[Link](https://zhuanlan.zhihu.com/p/433329525)] 
140 | - ICLR'22 Learn Locally, Correct Globally: A Distributed Algorithm for Training Graph Neural Networks [[Paper](https://arxiv.org/pdf/2111.08202.pdf)] [[Code](https://github.com/MortezaRamezani/llcg)] [[Link](https://zhuanlan.zhihu.com/p/536158540)]
141 | 
142 | ## Personalized and Heterogeneous Federated Learning in CV or NLP
143 | 
144 | - NeurIPS'18 Workshop Communication-Efficient On-Device Machine Learning: Federated Distillation and Augmentation under Non-IID Private Data [[Paper](https://arxiv.org/abs/1811.11479)] [No Code] [[Link](https://zhuanlan.zhihu.com/p/535652457)]
145 | - NeurIPS'19 Think Locally, Act Globally: Federated Learning with Local and Global Representations [[Paper](https://arxiv.org/pdf/2001.01523.pdf)] [[Code](https://github.com/pliang279/LG-FedAvg)] [[Link](https://zhuanlan.zhihu.com/p/497030361)]
146 | - arXiv'20 Adaptive Personalized Federated Learning [[Paper](https://arxiv.org/abs/2003.13461)] [No Code] [[Link](https://zhuanlan.zhihu.com/p/497269102)]
147 | - AAAI'21 Addressing Class Imbalance in Federated Learning [[Paper](https://arxiv.org/abs/2008.06217)] [[Code](https://github.com/balanced-fl/Addressing-Class-Imbalance-FL)] [[Link](https://zhuanlan.zhihu.com/p/443009189)] 
148 | 
149 | ## Theoretical Analysis of Federated Learning in CV or NLP
150 | 
151 | - JMLR'17 Communication-Efficient Learning of Deep Networks from Decentralized Data [[Paper](http://proceedings.mlr.press/v54/mcmahan17a/mcmahan17a.pdf)] [[Code](https://github.com/AshwinRJ/Federated-Learning-PyTorch)] [[Link](https://zhuanlan.zhihu.com/p/429370255)]
152 | - arXiv'19 Detailed comparison of communication efficiency of split learning and federated learning [[Paper](https://arxiv.org/pdf/1909.09145.pdf)] [[Link](https://zhuanlan.zhihu.com/p/435255850)] 
153 | - ICLR'20 On the Convergence of FedAvg on Non-IID Data [[Paper](https://arxiv.org/abs/1907.02189)] [[Code](https://github.com/lx10077/fedavgpy)] [[Link](https://zhuanlan.zhihu.com/p/500005337)]
154 | 
155 | ## CV or NLP Model Compression and Quantification in Federated Learning 
156 | 
157 | - NeurIPS'19 Workshop FedMD: Heterogenous Federated Learning via Model Distillation [[Paper](https://arxiv.org/pdf/1910.03581.pdf)] [[Code](https://github.com/diogenes0319/FedMD_clean)] [[Link](https://zhuanlan.zhihu.com/p/535687915?)]
158 | - NeurIPS'20 Group Knowledge Transfer: Federated Learning of Large CNNs at the Edge [[Paper](https://arxiv.org/pdf/2007.14513.pdf)] [[Code](https://fedml.ai/)] [[Link](https://zhuanlan.zhihu.com/p/536901871)]
159 | - arXiv'22 CDKT-FL: Cross-Device Knowledge Transfer using Proxy Dataset in Federated Learning [[Paper](https://arxiv.org/pdf/2204.01542.pdf)] [[Code](https://github.com/Agent2H/CDKT_FL)] [[Link](https://zhuanlan.zhihu.com/p/528950968)]
160 | 
161 | ## Transfer Federated Graph Learning and Graph Structure Federated Learning
162 | 
163 | - arXiv'19 Peer-to-Peer Federated Learning on Graphs [[Paper](https://arxiv.org/abs/1901.11173)] [No Code] [[Link](https://zhuanlan.zhihu.com/p/441944011)]
164 | - ICML'21 SpreadGNN: Serverless Multi task Federated Learning for Graph Neural Networks [[Paper](https://arxiv.org/abs/2106.02743)] [[Code](https://github.com/FedML-AI/SpreadGNN)] [[Link](https://zhuanlan.zhihu.com/p/429720860)] 
165 | - PPNA'21 ASFGNN: Automated Separated-Federated Graph Neural Network [[Paper](https://arxiv.org/abs/2011.03248)] [No Code] [[Link](https://zhuanlan.zhihu.com/p/431283541)]
166 | - IJCAI'21 Decentralized Federated Graph Neural Networks [[Paper](https://federated-learning.org/fl-ijcai-2021/FTL-IJCAI21_paper_20.pdf)] [No Code] [[Link](https://zhuanlan.zhihu.com/p/430508567)]
167 | - CVPR'21 Cluster-driven Graph Federated Learning over Multiple Domains [[Paper](https://arxiv.org/abs/2104.14628)] [No Code] [[Link](https://zhuanlan.zhihu.com/p/440527314)]
168 | - ICML'21 Personalized Federated Learning using Hypernetworks [[Paper](https://arxiv.org/abs/2103.04628)] [[Code](https://github.com/AvivSham/pFedHN)] [[Link](https://zhuanlan.zhihu.com/p/431130945)] 
169 | 
170 | ## Intra-Graph Horizontal Federated Learning
171 | 
172 | - arXiv'20 GraphFL: A Federated Learning Framework for Semi-Supervised Node Classification on Graphs [[Paper](https://arxiv.org/abs/2012.04187)] [No Code] [[Link](https://zhuanlan.zhihu.com/p/431479904)] 
173 | - NeurIPS'21 Subgraph Federated Learning with Missing Neighbor Generation [[Paper](https://arxiv.org/abs/2106.13430)] [[Code](https://github.com/zkhku/fedsage)] [[Link](https://zhuanlan.zhihu.com/p/430789355)] 
174 | - ICML'21 FedGNN: Federated Graph Neural Network for Privacy-Preserving Recommendation [[Paper](https://arxiv.org/abs/2102.04925)] [No Code] [[Link](https://zhuanlan.zhihu.com/p/428783383)]
175 | - arXiv'21 FedGL: Federated Graph Learning Framework with Global Self-Supervision [[Paper](https://arxiv.org/abs/2105.03170)] [No Code] [[Link](https://zhuanlan.zhihu.com/p/431049080)]
176 | - KDD'21 Cross-Node Federated Graph Neural Network for Spatio-Temporal Data Modeling [[Paper](https://arxiv.org/abs/2106.05223)] [[Code](https://github.com/mengcz13/KDD2021_CNFGNN)] [[Link](https://zhuanlan.zhihu.com/p/434839878)] 
177 | - CIKM'21 Federated Knowledge Graphs Embedding [[Paper](https://arxiv.org/abs/2105.07615)] [[Code](https://github.com/HKUST-KnowComp/FKGE)] [[Link](https://zhuanlan.zhihu.com/p/437895959)] 
178 | 
179 | ## Inter-Graph Horizontal Federated Learning
180 | 
181 | - NeurIPS'21 Federated Graph Classification over Non-IID Graphs [[Paper](https://arxiv.org/abs/2106.13423)] [[Code](https://github.com/Oxfordblue7/GCFL)] [[Link](https://zhuanlan.zhihu.com/p/430623053)] 
182 | 
183 | ## Vertical Federal Learning
184 | 
185 | - arXiv'21 A Vertical Federated Learning Framework for Graph Convolutional Network [[Paper](https://arxiv.org/abs/2106.11593)] [No Code] [[Link](https://zhuanlan.zhihu.com/p/431900470)] 
186 | - arXiv'21 Vertically Federated Graph Neural Network for Privacy-Preserving Node Classification [[Paper](https://arxiv.org/abs/2005.11903)] [No Code] [[Link](https://zhuanlan.zhihu.com/p/428406637)]
187 | 
188 | # Privacy Graph Neural Networks
189 | 
190 | - SIGSAC'16 Deep Learning with Differential Privacy [[Paper](https://arxiv.org/abs/1607.00133v1)] [[Code](https://github.com/lingyunhao/Deep-Learning-with-Differential-Privacy)] [[Link](https://zhuanlan.zhihu.com/p/419216660)]
191 | - ICLR'17 Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data [[Paper](https://arxiv.org/abs/1610.05755)] [[Code](https://github.com/kamathhrishi/PATE)] [[Link](https://zhuanlan.zhihu.com/p/423009101)]
192 | - ICLR'18 Scalable Private Learning With PATE [[Paper](https://arxiv.org/abs/1802.08908)] [[Code](https://github.com/kamathhrishi/PATE)] [[Link](https://zhuanlan.zhihu.com/p/423063552)]
193 | - arXiv'20  When Differential Privacy Meets Graph Neural Networks [[Paper](https://arxiv.org/pdf/2006.05535v1.pdf)]  [[Code](https://github.com/sisaman/LPGNN)]  [[Link](https://zhuanlan.zhihu.com/p/423868946)]
194 | - arXiv'21 Releasing Graph Neural Networks with Differential Privacy [[Paper](https://arxiv.org/abs/2109.08907)] [No Code] [[Link](https://zhuanlan.zhihu.com/p/424463019)]
195 | - arXiv'21 A Graph Federated Architecture with Privacy Preserving Learning [[Paper](https://arxiv.org/abs/2104.13215)] [No Code] [[Link](https://zhuanlan.zhihu.com/p/433728803)]
196 | - IJCAI'21 Secure Deep Graph Generation with Link Differential Privacy [[Paper](https://arxiv.org/abs/2005.00455v3)] [[Code](https://github.com/haonan3/Secure-Network-Release-with-Link-Privacy)] [[Link](https://zhuanlan.zhihu.com/p/417555475)]
197 | - CCS'21 Locally Private Graph Neural Networks [[Paper](https://arxiv.org/pdf/2006.05535.pdf)] [[Code](https://github.com/sisaman/LPGNN)] [[Link](https://zhuanlan.zhihu.com/p/423444455)]
198 | 
199 | # Survey and Framework Toolkits
200 | 
201 | - Graph library -- PyG、GarphGallery [[Link](https://zhuanlan.zhihu.com/p/420587332)]
202 | 
203 | - Graph library -- DIG、AutoGL、CogDL [[Link](https://zhuanlan.zhihu.com/p/422082239)]
204 | 
205 | - PyTorch Geometric（一）：数据加载 [[Link](https://zhuanlan.zhihu.com/p/425974734)]
206 | 
207 | - PyTorch Geometric（二）：模型搭建 [[Link](https://zhuanlan.zhihu.com/p/427083823)]
208 | 
209 | - 基于 GNN 的隐私计算（联邦学习）Review（二）[[Link](https://zhuanlan.zhihu.com/p/432071253)]
210 | 
211 | - 基于 GNN 的隐私计算（联邦学习）Review（三）[[Link](https://zhuanlan.zhihu.com/p/432126858)]
212 | 
213 | - introduction [[Link](https://zhuanlan.zhihu.com/p/416264898)]
214 | 
215 | - Local Differential Privacy: a tutorial [[Paper](https://arxiv.org/abs/1907.11908)] [[Link](https://zhuanlan.zhihu.com/p/416556008)]
216 | 
217 | - 本地化差分隐私研究综述 [[Paper](https://wenku.baidu.com/view/ca901cf8876fb84ae45c3b3567ec102de3bddf84?fr=xueshu)] [[Link](https://zhuanlan.zhihu.com/p/417209747)]
218 | 
219 | - 差分隐私 -- Laplace mechanism、Gaussian mechanism、Composition theorem [[Link](https://zhuanlan.zhihu.com/p/425732159)]
220 | 
221 | - 矩母函数 GMF 及矩的概念 -- 期望、方差、归一化矩、偏态、峰度 [[Link](https://zhuanlan.zhihu.com/p/425898950)] [[Reference](https://towardsdatascience.com/moment-generating-function-explained-27821a739035)]
222 | 
223 | - Moments Accountant 的理解 [[Link](https://zhuanlan.zhihu.com/p/425780267)] [[Reference](https://zhuanlan.zhihu.com/p/264779199)]
224 | 
225 | - 基于 GNN 的隐私计算（差分隐私）Review（一）[[Link](https://zhuanlan.zhihu.com/p/426267637)]
226 | 
227 | - Federated Machine Learning: Concept and Applications [[Paper](https://arxiv.org/abs/1902.04885)] [[Link](https://zhuanlan.zhihu.com/p/427770121)]
228 | 
229 | - arXiv'21 Graph4Rec: A Universal Toolkit with Graph Neural Networks for Recommender Systems [[Paper](https://arxiv.org/abs/2112.01035)] [[Code](https://github.com/PaddlePaddle/PGL/tree/main/apps/Graph4Rec)] [[Link](https://zhuanlan.zhihu.com/p/443243204)]
230 | 
231 | - arXiv'21 Federated Graph Learning - A Position Paper [[Paper](https://arxiv.org/abs/2105.11099)] [[Link](https://zhuanlan.zhihu.com/p/431934452)]
232 | 
233 | - arXiv'21 Federated Learning on Non-IID Data Silos: An Experimental Study [[Paper](https://arxiv.org/abs/2102.02079)] [[Code](https://github.com/Xtra-Computing/NIID-Bench)] [[Link](https://zhuanlan.zhihu.com/p/439561676)] 
234 | 
235 | - ICLR'21 FedGraphNN: A Federated Learning Benchmark System for Graph Neural Networks [[Paper](https://arxiv.org/abs/2104.07145)] [[Code](https://github.com/FedML-AI/FedGraphNN)] [[Link](https://zhuanlan.zhihu.com/p/429220636)] 
236 | 
237 | - arXiv‘22 Data Augmentation for Deep Graph Learning: A Survey  [[Paper](https://arxiv.org/pdf/2202.08235.pdf)] [No Code] [[Link](https://zhuanlan.zhihu.com/p/492146847)]
238 | 
239 | - arXiv'22 A Survey on Graph Structure Learning: Progress and Opportunities [[Paper](https://arxiv.org/pdf/2103.03036.pdf)] [No Code] [[Link](https://zhuanlan.zhihu.com/p/470614896)]
240 | 
241 |   
242 | 
243 |   
244 | 
245 | 


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