Graph Neural Networks (GNNs) have made rapid developments in the recent years. Due to
their great ability in modeling graph-structured data, GNNs are vastly used in various …

Data-centric AI, with its primary focus on the collection, management, and utilization of data
to drive AI models and applications, has attracted increasing attention in recent years. In this …

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

Graph Neural Networks (GNNs) have received increasing attention due to their ability to
learn from graph-structured data. However, their predictions are often not interpretable. Post …

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

Recent efforts in fake news detection have witnessed a surge of interest in using graph
neural networks (GNNs) to exploit rich social context. Existing studies generally leverage …

Graph Neural Networks (GNNs) have demonstrated great power for the semi-supervised
node classification task. However, most GNN methods are sensitive to the noise of graph …

Graph Neural Networks (GNNs) have achieved promising results in various tasks such as
node classification and graph classification. Recent studies find that GNNs are vulnerable to …

Abstract Graph Contrastive Learning (GCL) has emerged as a popular unsupervised graph
representation learning method. However, it has been shown that GCL is vulnerable to …

Graph Neural Networks (GNNs) have become widely-used models for semi-supervised
learning. However, the robustness of GNNs in the presence of label noise remains a largely …