44 citations · 150 across the 15 of their papers we have counts for
21 papers
G-Mixup: Graph Data Augmentation for Graph Classification
Xiaotian Han, Zhimeng Jiang, Ninghao Liu +1
This work develops \emph{mixup for graph data}. Mixup has shown superiority in improving the generalization and robustness of neural networks by interpolating features and labels b…
Geometric Graph Representation Learning via Maximizing Rate Reduction
Xiaotian Han, Zhimeng Jiang, Ninghao Liu +3
Learning discriminative node representations benefits various downstream tasks in graph analysis such as community detection and node classification. Existing graph representation…
MGAE: Masked Autoencoders for Self-Supervised Learning on Graphs
Qiaoyu Tan, Ninghao Liu, Xiao Huang +3
We introduce a novel masked graph autoencoder (MGAE) framework to perform effective learning on graph structure data. Taking insights from self-supervised learning, we randomly mas…
Defense Against Explanation Manipulation
Ruixiang Tang, Ninghao Liu, Fan Yang +2
Explainable machine learning attracts increasing attention as it improves transparency of models, which is helpful for machine learning to be trusted in real applications. However,…
Adaptive Label Smoothing To Regularize Large-Scale Graph Training
Kaixiong Zhou, Ninghao Liu, Fan Yang +5
Graph neural networks (GNNs), which learn the node representations by recursively aggregating information from its neighbors, have become a predominant computational tool in many d…
ExAD: An Ensemble Approach for Explanation-based Adversarial Detection
Raj Vardhan, Ninghao Liu, Phakpoom Chinprutthiwong +4
Recent research has shown Deep Neural Networks (DNNs) to be vulnerable to adversarial examples that induce desired misclassifications in the models. Such risks impede the applicati…