The papers of graph neural network, fairness and self-supervised learning
- GCC: Graph Contrastive Coding for Graph Neural Network Pre-Training. KDD 2020
- Graph Contrastive Learning with Adaptive Augmentation. arXiv 2020
- Graph contrastive learning with augmentations. NIPS 2020
- Motif-Driven Contrastive Learning of Graph Representations. arXiv 2020
- Pre-Training Graph Neural Networks for Cold-Start Users and Items Representation. arXiv 2020
- Distance Encoding: Design Provably More Powerful Neural Networks for Graph Representation Learning. NIPS 2020
- Graph Contrastive Learning with Augmentations. NIPS 2020
- Graph Meta Learning via Local Subgraphs. NIPS 2020
- Subgraph Neural Networks. NIPS 2020
- Factor Graph Neural Networks. NIPS 2020
- Implicit Graph Neural Networks. NIPS 2020
- Principal Neighbourhood Aggregation for Graph Nets. NIPS 2020
- Reliable Graph Neural Networks via Robust Aggregation. NIPS 2020
- Scattering GCN: Overcoming Oversmoothness in Graph Convolutional Networks. NIPS 2020
- Random Walk Graph Neural Networks. NIPS 2020
- Node Classification on Graphs with Few-Shot Novel Labels via Meta Transformed Network Embedding. NIPS 2020
- Iterative Deep Graph Learning for Graph Neural Networks: Better and Robust Node Embeddings. NIPS 2020
- Factorizable Graph Convolutional Networks. NIPS 2020
- Graph Random Neural Networks for Semi-Supervised Learning on Graphs. NIPS 2020
- What Makes for Good Views for Contrastive Learning?. NIPS 2020
- Debiased Contrastive Learning. NIPS 2020
- LoCo: Local Contrastive Representation Learning. NIPS 2020
- Self-paced Contrastive Learning with Hybrid Memory for Domain Adaptive Object Re-ID. NIPS 2020
- Contrastive learning of global and local features for medical image segmentation with limited annotations. NIPS 2020
- Few-shot Visual Reasoning with Meta-Analogical Contrastive Learning. NIPS 2020
- Supervised Contrastive Learning. NIPS 2020
- Hard Negative Mixing for Contrastive Learning. NIPS 2020
- How to Find Your Friendly Neighborhood: Graph Attention Deisgn With Self-Supervision.
- Are Graph Convolutional Networks Fully Exploring The Graph Structure?
- Local Clustering Graph Neural Networks.
- GraphCGAN: Convolutional Graph Neural Network with Generative Adversarial Networks.
- Transferable Feature Learning on Graphs Across Visual Domains.
- FASG: Feature Aggregation Self-training GCN for Semi-supervised Node Classification.
- GL-Disen: Global-Local disentanglement for unsupervised learning of graph-level representations.
- FLAG: Adversarial Data Augmentation for Graph Neural Networks
- Memory Augmented Design of Graph Neural Networks.
- Self-supervised Graph-level Representation Learning with Local and Global Structure.
- Learning Discrete Adaptive Receptive Fields for Graph Convolutional Networks.
- Don't stack layers in graph neural networks, wire them randomly.
- Combining Label Propagation and Simple Models out-performs Graph Neural Networks.
- DeeperGCN: Training Deeper GCNs with Generalized Aggregation Functions.
- GraphSAD: Learning Graph Representations with Structure-Attribute Disentanglement.
- AdaGCN: Adaboosting Graph Convolutional Networks into Deep Models.
- On Dyadic Fairness: Exploring and Mitigating Bias in Graph Connections.
- Multi-hop Attention Graph Neural Network.
- SLAPS: Self-Supervision Improves Structure Learning for Graph Neural Networks.
- Polynomial Graph Convolutional Networks.
- Adversarial Training using Contrastive Divergence.
- Training GANs with Stronger Augmentations via Contrastive Discriminator.
- Function Contrastive Learning of Transferable Representations.
- Improving Transformation Invariance in Contrastive Representation Learning.
- Decomposing Mutual Information for Representation Learning.
- A structural graph representation learning framework. WSDM 2021
- All You Need is Low (Rank): Defending Against Adversarial Attacks on Graphs. WSDM 2021
- Deep Multi-Graph Clustering via Attentive Cross-Graph Association. WSDM 2021
- Epidemic Graph Convolutional Network. WSDM 2021
- GREASE: A Generative Model for Relevance Search over Knowledge Graphs. WSDM 2021
- Initialization for Network Embedding: A Graph Partition Approach. WSDM 2021
- LouvainNE: Hierarchical Louvain Method for High Quality and Scalable Graph Embedding. WSDM 2021
- Nearly Linear Time Algorithm for Mean Hitting Times of Random Walks on a Graph. WSDM 2021
- Why Do Attributes Propagate in Graph Convolutional Neural Networks? AAAI 2021
- UAG: Uncertainty-‐Aware Attention Graph Neural Network for Defending Adversarial Attacks. AAAI 2021
- Deep Metric Learning with Graph Consistency. AAAI 2021
- Disentangled Multi-‐Relational Graph Convolutional Network for Pedestrian Trajectory Prediction. AAAI 2021
- Contrastive and Generative Graph Convolutional Networks for Graph-‐Based Semi-‐Supervised Learning. AAAI 2021
- Overcoming Catastrophic Forgetting in Graph Neural Networks. AAAI 2021
- Relative and Absolute Location Embedding for Few-‐Shot Node Classification on Graph. AAAI 2021
- Semi-‐Supervised Node Classification on Graphs: Markov Random Fields vs. Graph Neural Networks. AAAI 2021
- Heterogeneous Graph Structure Learning for Graph Neural Networks. AAAI 2021
- Unsupervised Domain Adaptation for Person Re-‐Identification via Heterogeneous Graph Alignment. AAAI 2021
- Overcoming Catastrophic Forgetting in Graph Neural Networks with Experience Replay. AAAI 2021
- Deep Graph Spectral Evolution Networks for Graph Topological Evolution. AAAI 2021
- Relation-‐Aware Graph Attention Model with Adaptive Self-‐Adversarial Training. AAAI 2021
- Reinforced Imitative Graph Representation Learning for Mobile User Profiling: An Adversarial Training Perspective. AAAI 2021
- Contrastive Self-‐Supervised Learning for Graph Classification. AAAI 2021
- Story Ending Generation with Multi-‐Level Graph Convolutional Networks over Dependency Trees. AAAI 2021
- Graph Neural Networks with Heterophily. AAAI 2021
- Learning Graph Neural Networks with Approximate Gradient Descent. AAAI 2021
- GraphMix: Improved Training of GNNs for Semi-‐Supervised Learning. AAAI 2021
- Power up! Robust Graph Convolutional Network via Graph Powering. AAAI 2021
- Learning to Pre-‐Train Graph Neural Networks. AAAI 2021
- Identity-‐Aware Graph Neural Networks. AAAI 2021
- Data Augmentation for Graph Neural Networks. AAAI 2021
- Beyond Low-‐Frequency Information in Graph Convolutional Networks. AAAI 2021
- Rethinking Graph Regularization forGraph Neural Networks. AAAI 2021