Guang Kou

Rectifying Pseudo Labels

2021

Graph Convolutional Networks (GCNs) are powerful representation learning methods for non-Euclidean data. Compared with the Euclidean data, labeling the non-Euclidean data is more expensive. Meanwhile, most existing GCNs only utilize few labeled data but ignore …

PATVD:Vulnerability Detection Based on Pre-training Techniques and Adversarial Training

2022

Software vulnerability detection has attracted more and more attention. Traditional vulnerability detection methods require a lot of expertise to define the vulnerability features, whereas the deep learning based methods can perform the feature extraction automatically. …

Graph Contrastive Learning with Adversarial Structure Refinement (GCL-ASR)

2024

The scarcity of labeled data in graph neural networks (GNNs) has driven the development of graph contrastive learning (GCL), which has become the most widely used method in unsupervised representation learning. At present, many GCL …