Leveraging labels in a supervised learning framework as prior knowledge to enhance
network anomaly detection has become a trend. Unfortunately, just a few labels are typically …

Few-shot Node Classification (FSNC) is a challenge in graph representation learning where
only a few labeled nodes per class are available for training. To tackle this issue, meta …

Graph structure learning (GSL), which aims to learn the adjacency matrix for graph neural
networks (GNNs), has shown great potential in boosting the performance of GNNs. Most …

Inspired by the extensive success of deep learning, graph neural networks (GNNs) have
been proposed to learn expressive node representations and demonstrated promising …

The success of graph neural networks (GNNs) provokes the question about
explainability:“Which fraction of the input graph is the most determinant of the prediction?” …

The history of artificial intelligence (AI) has witnessed the significant impact of high-quality
data on various deep learning models, such as ImageNet for AlexNet and ResNet. Recently …

We present a simple and yet effective interpolation-based regularization technique, aiming
to improve the generalization of Graph Neural Networks (GNNs) on supervised graph …

Nowadays, fake news easily propagates through online social networks and becomes a
grand threat to individuals and society. Assessing the authenticity of news is challenging …

Few-shot node classification is tasked to provide accurate predictions for nodes from novel
classes with only few representative labeled nodes. This problem has drawn tremendous …

Graph Neural Networks (GNNs), inspired by Convolutional Neural Networks (CNNs),
aggregate the message of nodes' neighbors and structure information to acquire expressive …