semi-supervised node classification

Sheaf Graph Neural Networks via PAC-Bayes Spectral Optimization AAAI 2026 Relation-Aware Diffusion for Heterogeneous Graphs with Partially Observed Features ICLR 2025 NodeMixup: Tackling Under-Reaching for Graph Neural Networks AAAI 2024 Revisiting Graph-Based Fraud Detection in Sight of Heterophily and Spectrum AAAI 2024 Similarity-Navigated Conformal Prediction for Graph Neural Networks NeurIPS 2024 Unsupervised Neighborhood Propagation Kernel Layers for Semi-supervised Node Classification AAAI 2024 Learning Robust Graph Neural Networks with Limited Supervision AISTATS 2023 Revisiting Graph Adversarial Attack and Defense From a Data Distribution Perspective ICLR 2023 Serving Graph Compression for Graph Neural Networks ICLR 2023 Towards Label Position Bias in Graph Neural Networks NeurIPS 2023 p-Laplacian Based Graph Neural Networks ICML 2022 Generalization Guarantee of Training Graph Convolutional Networks with Graph Topology Sampling ICML 2022 Is Homophily a Necessity for Graph Neural Networks? ICLR 2022 Rethinking the Promotion Brought by Contrastive Learning to Semi-Supervised Node Classification IJCAI 2022 Data Augmentation for Graph Neural Networks AAAI 2021 Explicit pairwise factorized graph neural network for semi-supervised node classification UAI 2021 Semi-Supervised Node Classification on Graphs: Markov Random Fields vs. Graph Neural Networks AAAI 2021 Topology-Imbalance Learning for Semi-Supervised Node Classification NeurIPS 2021 Coresets for Estimating Means and Mean Square Error with Limited Greedy Samples UAI 2020 Going Deep: Graph Convolutional Ladder-Shape Networks AAAI 2020 MR-GCN: Multi-Relational Graph Convolutional Networks based on Generalized Tensor Product IJCAI 2020 Scattering GCN: Overcoming Oversmoothness in Graph Convolutional Networks NeurIPS 2020 Fisher-Bures Adversary Graph Convolutional Networks UAI 2019