1 citations · 1 across the 4 of their papers we have counts for
4 papers
Mitigating Structural Overfitting: A Distribution-Aware Rectification Framework for Missing Feature Imputation
Yifan Song, Fenglin Yu, Yihong Luo +4
Incomplete node features are ubiquitous in real-world scenarios such as user profiling and cold-start recommendation, which severely hinders the practical deployment of graph learn…
Fast Graph Sharpness-Aware Minimization for Enhancing and Accelerating Few-Shot Node Classification
Yihong Luo, Yuhan Chen, Siya Qiu +5
Graph Neural Networks (GNNs) have shown superior performance in node classification. However, GNNs perform poorly in the Few-Shot Node Classification (FSNC) task that requires robu…
Energy-Calibrated VAE with Test Time Free Lunch
Yihong Luo, Siya Qiu, Xingjian Tao +2
In this paper, we propose a novel generative model that utilizes a conditional Energy-Based Model (EBM) for enhancing Variational Autoencoder (VAE), termed Energy-Calibrated VAE (E…
LSGNN: Towards General Graph Neural Network in Node Classification by Local Similarity
Yuhan Chen, Yihong Luo, Jing Tang +4
Heterophily has been considered as an issue that hurts the performance of Graph Neural Networks (GNNs). To address this issue, some existing work uses a graph-level weighted fusion…