- arXiv'21 Graph4Rec: A Universal Toolkit with Graph Neural Networks for Recommender Systems [Paper] [Code] [Link]
- arXiv'21 GIST: Distributed Training for Large-Scale Graph Convolutional Networks [Paper] [Code] [Link]
- arXiv'21 Graph Learning with 1D Convolutions on Random Walks [Paper] [Code] [Link]
- ICML'19 Simplifying Graph Convolutional Networks [Paper] [Code] [Link]
- ICLR'19 Predict Then Propagate: Graph Neural Networks Meet Personalized PageRank [Paper] [Code] [Link]
- arXiv'21 Graph Attention Multi-Layer Perceptron [Paper] [Code] [Link]
- NeurIPS‘21 Node Dependent Local Smoothing for Scalable Graph Learning [Paper] [Code] [Link]
- ICLR'19 How Powerful are Graph Neural Networks? [Paper] [Code] [Link] √
- ICLR'22 A New Perspective on "How Graph Neural Networks Go Beyond Weisfeiler-Lehman?" [Paper] [No Code] [Link] √
- NeurIPS'17 Inductive Representation Learning on Large Graphs [Paper] [Code] [Link]
- ICLR'18 FASTGCN: Fast Learning With Graph Convolutional Networks Via Importance Sampling [Paper] [Code] [Link]
- arXiv'21 GIST: Distributed Training for Large-Scale Graph Convolutional Networks [Paper] [Code] [Link]
- KDD'14 DeepWalk: Online Learning of Social Representations [Paper] [Code] [Link]
- WWW'15 LINE: Large-scale Information Network Embedding [Paper] [Code] [Link]
- KDD'16 node2vec: Scalable Feature Learning for Networks [Paper] [Code] [Link]
- NeurIPS'13 Distributed Representations of Words and Phrases and their Compositionality [Paper] [Code] [Link]
- KDD'16 Structural Deep Network Embedding [Paper] [Code] [Link]
- arXiv'21 Graph Learning with 1D Convolutions on Random Walks [Paper] [Code] [Link]
- arXiv'22 A Survey on Graph Structure Learning: Progress and Opportunities [Paper] [No Code] [Link]
- arXiv'20 Non-Local Graph Neural Networks [Paper] [No Code] [Link] √
- WSDM'21 GraphSMOTE: Imbalanced Node Classification on Graphs with Graph Neural Networks [Paper] [Code] [Link] √
- IJCAI'21 Multi-Class Imbalanced Graph Convolutional Network Learning [Paper] [Code] [Link] √
- WWW'21 Graph Contrastive Learning with Adaptive Augmentation [Paper] [Code] [Link] √
- AAAI'21 Data Augmentation for Graph Neural Networks [Paper] [Code] [Link] √
- AAAI'22 Regularizing Graph Neural Networks via Consistency-Diversity Graph Augmentations [Paper] [No Code] [Link] ■
- KDD'20 NodeAug: Semi-Supervised Node Classification with Data Augmentation [Paper] [No Code] [Link] ■
- AAAI'21 GraphMix: Improved Training of GNNs for Semi-Supervised Learning [Paper] [Code] [Link] ■
- arXiv'21 Local Augmentation for Graph Neural Networks [Paper] [No Code] [Link] ■
- introduction [Link]
- Local Differential Privacy: a tutorial [Paper] [Link]
- 本地化差分隐私研究综述 [Paper] [Link]
- 差分隐私 -- Laplace mechanism、Gaussian mechanism、Composition theorem [Link]
- 矩母函数 GMF 及矩的概念 -- 期望、方差、归一化矩、偏态、峰度 [Link] [Reference]
- Moments Accountant 的理解 [Link] [Reference]
- 基于 GNN 的隐私计算(差分隐私)Review(一)[Link]
- SIGSAC'16 Deep Learning with Differential Privacy [Paper] [Code] [Link]
- ICLR'17 Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data [Paper] [Code] [Link]
- ICLR'18 Scalable Private Learning With PATE [Paper] [Code] [Link]
- CCS'21 Locally Private Graph Neural Networks [Paper] [Code] [Link]
- arXiv'20 When Differential Privacy Meets Graph Neural Networks [Paper] [Code] [Link]
- arXiv'21 Releasing Graph Neural Networks with Differential Privacy [Paper] [No Code] [Link]
- arXiv'21 Federated Graph Learning - A Position Paper [Paper] [Link]
- Big Data'19 SGNN: A Graph Neural Network Based Federated Learning Approach by Hiding Structure [Paper] [No Code] [Link]
- 基于 GNN 的隐私计算(联邦学习)Review(二)[Link]
- 基于 GNN 的隐私计算(联邦学习)Review(三)[Link]
- ICML'21 SpreadGNN: Serverless Multi task Federated Learning for Graph Neural Networks [Paper] [Code] [Link] (
$\star$ ) ▲ - NeurIPS'21 Federated Graph Classification over Non-IID Graphs [Paper] [Code] [Link] √
- arXiv'20 Federated Dynamic GNN with Secure Aggregation [Paper] [No Code] [Link] ($ \star$) ■
- NeurIPS'21 Subgraph Federated Learning with Missing Neighbor Generation [Paper] [Code] [Link] √
- ICML'21 FedGNN: Federated Graph Neural Network for Privacy-Preserving Recommendation [Paper] [No Code] [Link] (
$\star$ ) √ - arXiv'21 FedGL: Federated Graph Learning Framework with Global Self-Supervision [Paper] [No Code] [Link] (
$\star$ ) √ - PPNA'21 ASFGNN: Automated Separated-Federated Graph Neural Network [Paper] [No Code] [Link] (
$\star$ ) ▲ - TSIPN'21 Distributed Training of Graph Convolutional Networks [Paper] [No Code] [Link] √
- KDD'21 Cross-Node Federated Graph Neural Network for Spatio-Temporal Data Modeling [Paper] [Code] [Link] ■
- arXiv'21 A Vertical Federated Learning Framework for Graph Convolutional Network [Paper] [No Code] [Link] (
$\star$ ) ■ - arXiv'21 Vertically Federated Graph Neural Network for Privacy-Preserving Node Classification [Paper] [No Code] [Link] (
$\star$ ) ▲ - CIKM'21 Federated Knowledge Graphs Embedding [Paper] [Code] [Link] ($ \star $) ▲
- IJCAI'21 Decentralized Federated Graph Neural Networks [Paper] [No Code] [Link] (
$\star$ ) √ - ICML'21 SpreadGNN: Serverless Multi task Federated Learning for Graph Neural Networks [Paper] [Code] [Link] (
$\star$ ) ▲ - TSIPN'21 Distributed Training of Graph Convolutional Networks [Paper] [No Code] [Link] √
- arXiv'21 A Graph Federated Architecture with Privacy Preserving Learning [Paper] [No Code] [Link] ($ \star$) ▲
- KDD'21 Cross-Node Federated Graph Neural Network for Spatio-Temporal Data Modeling [Paper] [Code] [Link] ■
- CVPR'21 Cluster-driven Graph Federated Learning over Multiple Domains [Paper] [No Code] [Link] ▲
- arXiv'19 Peer-to-Peer Federated Learning on Graphs [Paper] [No Code] [Link] ■
- ICML'21 Personalized Federated Learning using Hypernetworks [Paper] [Code] [Link] ▲
- arXiv'20 GraphFL: A Federated Learning Framework for Semi-Supervised Node Classification on Graphs [Paper] [No Code] [Link] ▲
- ICLR'21 FedGraphNN: A Federated Learning Benchmark System for Graph Neural Networks [Paper] [Code] [Link] ■
- Federated Machine Learning: Concept and Applications [Paper] [Link]
- JMLR'17 Communication-Efficient Learning of Deep Networks from Decentralized Data [Paper] [Code] [Link] FedAvg √
- arXiv'21 Federated Learning on Non-IID Data Silos: An Experimental Study [Paper] [Code] [Link] √
- AAAI'21 Addressing Class Imbalance in Federated Learning [Paper] [Code] [Link] √
- arXiv'20 Non-IID Graph Neural Networks [Paper] [No Code] [Link] ▲