Awesome GNN Research

🔥 Top AI&ML&DM Conferences or Journals and arXiv Prep.

CVPR, ICCV, ECCV, ACL, MM, etc

AAAI, IJCAI, SIGIR, ICDE, SIGMOD, etc

KDD, WWW, ICLR, ICML, NeurIPS, TKDE, etc

⭐ Research Keywords

Scalable Graph Neural Networks, Federated Graph Learning, Recommender system Based on GNN

Wizardship

Scalable Graph Neural Networks
Federated Graph Learning
Privacy Graph Neural Networks
Survey and Framework Toolkits

Scalable Graph Neural Networks

Graph Embedding

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]

Linear-model based Graph Neural Networks

ICML'19 Simplifying Graph Convolutional Networks [Paper] [Code] [Link]
ICLR'19 Predict Then Propagate: Graph Neural Networks Meet Personalized PageRank [Paper] [Code] [Link]
arXiv'20 Scalable Graph Neural Networks for Heterogeneous Graphs [Paper] [Code] [Link]
arXiv'20 Unifying Graph Convolutional Neural Networks and Label Propagation [Paper] [Code] [Link]
NeurIPS‘21 Node Dependent Local Smoothing for Scalable Graph Learning [Paper] [Code] [Link]
arXiv'21 Scalable and Adaptive Graph Neural Networks with Self-Label-Enhanced Training [Paper] [Code] [Link]
ICLR'21 Combining Label Propagation and Simple Models Out-performs Graph Neural Networks [Paper] [Code] [Link]
ICLR'22 Node Feature Extraction by Self-Supervised Multi-scale Neighborhood Prediction [Paper] [Code] [Link]
arXiv'22 SCR: Training Graph Neural Networks with Consistency Regularization [Paper] [Code] [Link]
KDD'22 Graph Attention MLP with Reliable Label Utilization [Paper] [Code] [Link]

Sampling based Graph Neural Networks

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]

Model Compression and Quantification

NeurIPS'14 Distilling the Knowledge in a Neural Network [Paper] [Code] [Link]
ICLR'15 FitNets: Hints for Thin Deep Nets [Paper] [No Code] [Link]
ICLR'17 Paying More Attention to Attention: Improving The Performance of Convolutional Neural Networks via Attention Transfer [Paper] [Code] [Link]
ICCV'19 Similarity-Preserving Knowledge Distillation [Paper] [No Code] [Link]
ICCV'19 Be Your Own Teacher: Improve the Performance of Convolutional Neural Networks via Self Distillation [Paper] [Code] [Link]
CVPR'21 Distilling Knowledge via Knowledge Review [Paper] [Code] [Link]
CVPR'21 Distill on the Go: Online knowledge distillation in self-supervised learning [Paper] [Code] [Link]
CVPR'22 Decoupled Knowledge Distillation [Paper] [Code] [Link]

CVPR'20 Distilling Knowledge from Graph Convolutional Networks [Paper] [Code] [Link]
SIGMOD'20 Reliable Data Distillation on Graph Convolutional Network [Paper] [No Code] [Link]
KDD'20 TinyGNN: Learning Efficient Graph Neural Networks [Paper] [No Code] [Link]
arXiv’21 Distilling Self-Knowledge From Contrastive Links to Classify Graph Nodes Without Passing Messages [Paper] [Code] [Link]
MICCAI'21 GKD: Semi-supervised Graph Knowledge Distillation for Graph-Independent Inference [Paper] [Code] [Link]
CVPR'21 Bi-GCN: Binary Graph Convolutional Network [Paper] [Code] [Link]
IJCAI'21 Graph-Free Knowledge Distillation for Graph Neural Networks [Paper] [Code] [Link]
IJCAI'21 On Self-Distilling Graph Neural Network [Paper] [No Code] [Link]
ICLR'21 On Graph Neural Networks versus Graph-Augmented MLPs [Paperr] [Code] [Link]
WWW'21 Extract the Knowledge of Graph Neural Networks and Go Beyond it: An Effective Knowledge Distillation Framework [Paper] [Code] [Link]
KDD'21 ROD: Reception-aware Online Distillation for Sparse Graphs [Paper] [Code] [Link]
arXiv'22 On Representation Knowledge Distillation for Graph Neural Networks [Paper] [No Code] [Link]
AAAI'22 Workshop Scalable Consistency Training for Graph Neural Networks via Self-Ensemble Self-Distillation [Paper] [No Code] [Link]
ICLR'22 Graph-less Neural Networks: Teaching Old MLPs New Tricks via Distillation [Paper] [Code] [Link]
KDD'22 Compressing Deep Graph Neural Networks via Adversarial Knowledge Distillation [Paper] [Code] [Link]
ICLR'22 Cold Brew Distilling Graph Node Representations with Incomplete or Missing Neighborhoods [Paper] [Code] [Link]
WSDM'22 Deep Graph-level Anomaly Detection by Glocal Knowledge Distillation [Paper] [Code] [Link]

Efficient Architecture and Paradigm

ICLR'19 How Powerful are Graph Neural Networks? [Paper] [Code] [Link]
NeurIPS'20 Graph Random Neural Networks for Semi-Supervised Learning on Graphs [Paper] [Code] [Link]
arXiv'21 Graph Learning with 1D Convolutions on Random Walks [Paper] [Code] [Link]
WSDM'21 Node Similarity Preserving Graph Convolutional Networks [Paper] [Code] [Link]
ICLR'22 A New Perspective on "How Graph Neural Networks Go Beyond Weisfeiler-Lehman?" [Paper] [No Code] [Link]
KDD'22 Model Degradation Hinders Deep Graph Neural Networks [Paper] [Code] [Link]
KDD'22 Feature Overcorrelation in Deep Graph Neural Networks: A New Perspective [Paper] [Code] [Link]

Graph Data Augmentation

KDD'20 NodeAug: Semi-Supervised Node Classification with Data Augmentation [Paper] [No Code] [Link]
arXiv'21 Local Augmentation for Graph Neural Networks [Paper] [No Code] [Link]
AAAI'21 Data Augmentation for Graph Neural Networks [Paper] [Code] [Link]
WWW'21 Graph Contrastive Learning with Adaptive Augmentation [Paper] [Code] [Link]
AAAI'22 Regularizing Graph Neural Networks via Consistency-Diversity Graph Augmentations [Paper] [No Code] [Link]
AAAI'21 GraphMix: Improved Training of GNNs for Semi-Supervised Learning [Paper] [Code] [Link]
CVPR'22 Robust Optimization as Data Augmentation for Large-scale Graphs [Paper] [Code] [Link]
AAAI'22 SAIL: Self-Augmented Graph Contrastive Learning [Paper] [No Code] [Link]

Imbalance Graph Neural Networks

arXiv'20 Non-Local Graph Neural Networks [Paper] [No Code] [Link]
arXiv'20 Non-IID 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]
NeurIPS’21 Shift-Robust GNNs: Overcoming the Limitations of Localized Graph Training Data [Paper] [Code] [Link]

Federated Graph Learning

Big Data'19 SGNN: A Graph Neural Network Based Federated Learning Approach by Hiding Structure [Paper] [No Code] [Link]
arXiv'20 Federated Dynamic GNN with Secure Aggregation [Paper] [No Code] [Link]
arXiv'21 GIST: Distributed Training for Large-Scale Graph Convolutional Networks [Paper] [Code] [Link]
TSIPN'21 Distributed Training of Graph Convolutional Networks [Paper] [No Code] [Link]
ICLR'22 Learn Locally, Correct Globally: A Distributed Algorithm for Training Graph Neural Networks [Paper] [Code] [Link]

Personalized and Heterogeneous Federated Learning in CV or NLP

NeurIPS'18 Workshop Communication-Efficient On-Device Machine Learning: Federated Distillation and Augmentation under Non-IID Private Data [Paper] [No Code] [Link]
NeurIPS'19 Think Locally, Act Globally: Federated Learning with Local and Global Representations [Paper] [Code] [Link]
arXiv'20 Adaptive Personalized Federated Learning [Paper] [No Code] [Link]
AAAI'21 Addressing Class Imbalance in Federated Learning [Paper] [Code] [Link]

Theoretical Analysis of Federated Learning in CV or NLP

JMLR'17 Communication-Efficient Learning of Deep Networks from Decentralized Data [Paper] [Code] [Link]
arXiv'19 Detailed comparison of communication efficiency of split learning and federated learning [Paper] [Link]
ICLR'20 On the Convergence of FedAvg on Non-IID Data [Paper] [Code] [Link]

CV or NLP Model Compression and Quantification in Federated Learning

NeurIPS'19 Workshop FedMD: Heterogenous Federated Learning via Model Distillation [Paper] [Code] [Link]
NeurIPS'20 Group Knowledge Transfer: Federated Learning of Large CNNs at the Edge [Paper] [Code] [Link]
arXiv'22 CDKT-FL: Cross-Device Knowledge Transfer using Proxy Dataset in Federated Learning [Paper] [Code] [Link]

Transfer Federated Graph Learning and Graph Structure Federated Learning

arXiv'19 Peer-to-Peer Federated Learning on Graphs [Paper] [No Code] [Link]
ICML'21 SpreadGNN: Serverless Multi task Federated Learning for Graph Neural Networks [Paper] [Code] [Link]
PPNA'21 ASFGNN: Automated Separated-Federated Graph Neural Network [Paper] [No Code] [Link]
IJCAI'21 Decentralized Federated Graph Neural Networks [Paper] [No Code] [Link]
CVPR'21 Cluster-driven Graph Federated Learning over Multiple Domains [Paper] [No Code] [Link]
ICML'21 Personalized Federated Learning using Hypernetworks [Paper] [Code] [Link]

Intra-Graph Horizontal Federated Learning

arXiv'20 GraphFL: A Federated Learning Framework for Semi-Supervised Node Classification on Graphs [Paper] [No Code] [Link]
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]
arXiv'21 FedGL: Federated Graph Learning Framework with Global Self-Supervision [Paper] [No Code] [Link]
KDD'21 Cross-Node Federated Graph Neural Network for Spatio-Temporal Data Modeling [Paper] [Code] [Link]
CIKM'21 Federated Knowledge Graphs Embedding [Paper] [Code] [Link]

Inter-Graph Horizontal Federated Learning

NeurIPS'21 Federated Graph Classification over Non-IID Graphs [Paper] [Code] [Link]

Vertical Federal Learning

arXiv'21 A Vertical Federated Learning Framework for Graph Convolutional Network [Paper] [No Code] [Link]
arXiv'21 Vertically Federated Graph Neural Network for Privacy-Preserving Node Classification [Paper] [No Code] [Link]

Privacy Graph Neural Networks

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]
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 A Graph Federated Architecture with Privacy Preserving Learning [Paper] [No Code] [Link]
IJCAI'21 Secure Deep Graph Generation with Link Differential Privacy [Paper] [Code] [Link]
CCS'21 Locally Private Graph Neural Networks [Paper] [Code] [Link]

Survey and Framework Toolkits

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]
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]
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]
Federated Machine Learning: Concept and Applications [Paper] [Link]
arXiv'21 Graph4Rec: A Universal Toolkit with Graph Neural Networks for Recommender Systems [Paper] [Code] [Link]
arXiv'21 Federated Graph Learning - A Position Paper [Paper] [Link]
arXiv'21 Federated Learning on Non-IID Data Silos: An Experimental Study [Paper] [Code] [Link]
ICLR'21 FedGraphNN: A Federated Learning Benchmark System for Graph Neural Networks [Paper] [Code] [Link]
arXiv‘22 Data Augmentation for Deep Graph Learning: A Survey [Paper] [No Code] [Link]
arXiv'22 A Survey on Graph Structure Learning: Progress and Opportunities [Paper] [No Code] [Link]

fengduqianhe/Awesome-GNN-Research