yjnjerry / Awesome-GNN-Research

My future research

Awesome-GNN-Research

1. Scalable GNN

1.0 GNN library

• arXiv'21 Graph4Rec: A Universal Toolkit with Graph Neural Networks for Recommender Systems [Paper] [Code] [Link]

1.1 Efficient and Scalable GNN Architectures

• 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] √

1.2 Large-scale Graphs and Sampling Techniques

• 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]

1.3 Knowledge Distillation for GNNs

1.4 Neural Architecture Search for GNNs

1.5 Industrial Applications and Systems

1.6 Transfer Learning of GNNs

1.7 Multi-Task Learning

1.8 Graph Embedding Based on Random Walk √

• 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]

1.9 Non-IID and Graph Data Adaptive Augmentation

• 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] ■

2. GNN + (Local) Differential Privacy

2.1 Overview and Survey

• 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]

2.2 Important Algorithms (Principles and Framework)

• 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]

2.3 DP with Generative Model (Graph Generation)

• IJCAI'21 Secure Deep Graph Generation with Link Differential Privacy [Paper] [Code] [Link]

2.4 DP/LDP with Graph representation learning

• 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]

3. Federated Learning Based on GNN

3.1 Summary and Algorithms

• 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]

3.2 Inter-graph FL

• 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$) ■

3.3 Horizontal Intra-graph FL

• 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] ■

3.4 Vertical Intra-graph FL

• 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 $) ▲

3.5 Graph-structured FL

• 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] ■

3.6 Personalized Federated Learning

• 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] ▲

3.7 GraphFL Benchmark System

• ICLR'21 FedGraphNN: A Federated Learning Benchmark System for Graph Neural Networks [Paper] [Code] [Link] ■

4. Federated Learning

• Federated Machine Learning: Concept and Applications [Paper] [Link]
• JMLR'17 Communication-Efficient Learning of Deep Networks from Decentralized Data [Paper] [Code] [Link] FedAvg √

4.1 Non-IID

• 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] ▲

4.2 Communication Efficiency

• arXiv'19 Detailed comparison of communication efficiency of split learning and federated learning [Paper] [Link] ▲

5. GNN library

• Graph library -- PyG、GarphGallery [Link]
• Graph library -- DIG、AutoGL、CogDL [Link]
• PyTorch Geometric（一）：数据加载 [Link]
• PyTorch Geometric（二）：模型搭建 [Link]