"node classification" Papers
22 papers found
Diffusing to the Top: Boost Graph Neural Networks with Minimal Hyperparameter Tuning
Lequan Lin, Dai Shi, Andi Han et al.
Joint Graph Rewiring and Feature Denoising via Spectral Resonance
Jonas Linkerhägner, Cheng Shi, Ivan Dokmanić
Preference-driven Knowledge Distillation for Few-shot Node Classification
Xing Wei, Chunchun Chen, Rui Fan et al.
Amortized Variational Deep Kernel Learning
Alan Matias, César Lincoln Mattos, Joao Paulo Gomes et al.
Class-Imbalanced Graph Learning without Class Rebalancing
Zhining Liu, Ruizhong Qiu, Zhichen Zeng et al.
Curriculum-Enhanced Residual Soft An-Isotropic Normalization for Over-Smoothness in Deep GNNs
Jin Li, Qirong Zhang, Shuling Xu et al.
Feature Distribution on Graph Topology Mediates the Effect of Graph Convolution: Homophily Perspective
Soo Yong Lee, Sunwoo Kim, Fanchen Bu et al.
GATE: How to Keep Out Intrusive Neighbors
Nimrah Mustafa, Rebekka Burkholz
Graph Attention Retrospective
Kimon Fountoulakis, Amit Levi, Shenghao Yang et al.
Graph Contrastive Invariant Learning from the Causal Perspective
9672 Yanhu Mo, Xiao Wang, Shaohua Fan et al.
Graph Neural Networks with a Distribution of Parametrized Graphs
See Hian Lee, Feng Ji, Kelin Xia et al.
Graph Neural Stochastic Diffusion for Estimating Uncertainty in Node Classification
Xixun Lin, Wenxiao Zhang, Fengzhao Shi et al.
Improving Expressive Power of Spectral Graph Neural Networks with Eigenvalue Correction
Kangkang Lu, Yanhua Yu, Hao Fei et al.
Mitigating Label Noise on Graphs via Topological Sample Selection
Yuhao Wu, Jiangchao Yao, Xiaobo Xia et al.
Pairwise Alignment Improves Graph Domain Adaptation
Shikun Liu, Deyu Zou, Han Zhao et al.
PC-Conv: Unifying Homophily and Heterophily with Two-Fold Filtering
Bingheng Li, Erlin Pan, Zhao Kang
Poincaré Differential Privacy for Hierarchy-Aware Graph Embedding
Yuecen Wei, Haonan Yuan, Xingcheng Fu et al.
Rethinking Independent Cross-Entropy Loss For Graph-Structured Data
Rui Miao, Kaixiong Zhou, Yili Wang et al.
Uncertainty for Active Learning on Graphs
Dominik Fuchsgruber, Tom Wollschläger, Bertrand Charpentier et al.
Unraveling the Impact of Heterophilic Structures on Graph Positive-Unlabeled Learning
Yuhao Wu, Jiangchao Yao, Bo Han et al.
Unsupervised Parameter-free Simplicial Representation Learning with Scattering Transforms
Hiren Madhu, Sravanthi Gurugubelli, Sundeep Prabhakar Chepuri
Verifying message-passing neural networks via topology-based bounds tightening
Christopher Hojny, Shiqiang Zhang, Juan Campos et al.