In recent years, graph neural networks (GNNs) have been successfully applied in many
fields due to their characteristics of neighborhood aggregation and have achieved state-of …

Data augmentation has recently seen increased interest in graph machine learning given its
demonstrated ability to improve model performance and generalization by added training …

Abstract Graph Structure Learning (GSL) has recently garnered considerable attention due
to its ability to optimize both the parameters of Graph Neural Networks (GNNs) and the …

Graph Neural Network (GNN) based social recommendation models improve the prediction
accuracy of user preference by leveraging GNN in exploiting preference similarity contained …

Abstract Graph Neural Networks (GNNs) have emerged as the de facto standard for
representation learning on graphs, owing to their ability to effectively integrate graph …

Graph Structure Learning (GSL) focuses on capturing intrinsic dependencies and
interactions among nodes in graph-structured data by generating novel graph structures …

Knowledge graph (KG) question generation (QG) aims to generate natural language
questions from KGs and target answers. Previous works mostly focus on a simple setting that …

As one of the most common tasks in graph data analysis, node classification is frequently
solved by using graph structure learning (GSL) techniques to optimize graph structures and …

Self-supervised learning on heterogeneous graphs has gained significant attention as it
eliminates the need for manual labeling. However, most existing researches focus on …

With the rapid advancement of 5G technology, the Internet of Things (IoT) has entered a new
phase of applications and is rapidly becoming a significant force in promoting economic …