- CVPR-19 Semi-supervised Learning with Graph Learning-Convolutional Networks
- ICDM-19 Learning Robust Representations with Graph Denoising Policy Network
- IJCAI-19 Topology Optimization based Graph Convolutional Network (TO-GNN)
- ICML-19 Learning Discrete Structures for Graph Neural Networks (LDS)
- AAAI-20 Measuring and Relieving the Over-smoothing Problem for Graph Neural Networks from the Topological View (AdaEdge)
- IJCAI-20 When Do GNNs Work: Understanding and Improving Neighborhood Aggregation
- ICML-20 Robust Graph Representation Learning via Neural Sparsification
- ICML-20 Continuous Graph Neural Networks (CGNN)
- KDD-20 Graph Structure Learning for Robust Graph Neural Networks (ProGNN)
- NIPS-20 Iterative Deep Graph Learning for Graph Neural Networks: Better and Robust Node Embeddings
- ICLR-21-submit VEM-GCN: Topology Optimization with Variational EM for Graph Convolutional Networks
- ICLR-21-submit Unifying Graph Convolutional Neural Networks and Label Propagation
- ICLR-21-submit Learning Discrete Adaptive Receptive Fields for Graph Convolutional Networks
- AAAI-18 Deeper Insights into Graph Convolutional Networks for Semi-Supervised Learning
- ICML-18 Representation Learning on Graphs with Jumping Knowledge Network
- AAAI-20 Measuring and Relieving the Over-smoothing Problem for Graph Neural Networks from the Topological View
- IJCAI-20 When Do GNNs Work: Understanding and Improving Neighborhood Aggregation
- ICLR-20 Measuring and Improving the Use of Graph Information in Graph Neural Networks
- ICLR-20 DropEdge: Towards Deep Graph Convolutional Networks on Node Classification
- ICLR-20 PairNorm: Tackling Oversmoothing in GNNs
- ICLR-20 Graph Neural Networks Exponentially Lose Expressive Power for Node Classification
- ICLR-20-workshop A Note on Over-Smoothing for Graph Neural Networks
- ICML-20 Simple and Deep Graph Convolutional Networks
- KDD-20 Towards Deeper Graph Neural Networks
- NIPS-20 Towards Deeper Graph Neural Networks with Differentiable Group Normalization
- NIPS-20 Scattering GCN: Overcoming Oversmoothness in Graph Convolutional Networks
- NIPS-20 Optimization and Generalization of Shallow Neural Networks with Quadratic Activation Functions
- NIPS-20-submit GraphMix: Regularized Training of Graph Neural Networks for Semi-Supervised Learning
- NIPS-20 Graph Random Neural Network for Semi-Supervised Learning on Graphs
- ICML-19-Workshop Batch Virtual Adversarial Training for Graph Convolutional Networks
- NIPS-19 Graph Agreement Models for Semi-Supervised Learning
- SIGIR-20 Label-Consistency based Graph Neural Networks for Semi-supervised Node Classification
- AAAI-20 Collaborative Graph Convolutional Networks: Unsupervised Learning Meets Semi-Supervised Learning
- AAAI-20 Multi-Stage Self-Supervised Learning for Graph Convolutional Networks on Graphs with Few Labeled Nodesa
- IJCAI-20 Multi-Class Imbalanced Graph Convolutional Network Learning
- ICML-20 When Does Self-Supervision Help Graph Convolutional Networks?
- ICLR-21-submit Towards Robust Graph Neural Networks against Label Noise
- KDD-18 Billion-scale Commodity Embedding for E-commerce Recommendation in Alibaba
- KDD-18 Graph convolutional neural networks for web-scale recommender systems (pinsage)
- WWW-19 Graph Neural Networks for Social Recommendation
- SIGIR-19 Neural Graph Collaborative Filtering
- SIGIR-20 LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation
- AAAI-20 Revisiting Graph based Collaborative Filtering: A Linear Residual Graph Convolutional Network Approach
- AAAI-20 Multi-Component Graph Convolutional Collaborative Filtering
- ICML-20 Graph Convolutional Network for Recommendation with Low-pass Collaborative Filters