In recent years, neural architecture-based recommender systems have achieved
tremendous success, but they still fall short of expectation when dealing with highly sparse …

Deep graph learning has achieved remarkable progresses in both business and scientific
areas ranging from finance and e-commerce, to drug and advanced material discovery …

Recently," pre-training and fine-tuning''has been adopted as a standard workflow for many
graph tasks since it can take general graph knowledge to relieve the lack of graph …

Graph-contrastive learning has aided the development of unsupervised graph
representation learning, comparable to supervised models in terms of performance …

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 …

Motivation Predicting the associations between human microbes and drugs (MDAs) is one
critical step in drug development and precision medicine areas. Since discovering these …

The emergence of Graph Neural Networks (GNNs) in graph data analysis and their
deployment on Machine Learning as a Service platforms have raised critical concerns about …

With the great popularity of Graph Neural Networks (GNNs), their robustness to adversarial
topology attacks has received significant attention. Although many attack methods have …

As Graph Neural Networks (GNNs) become increasingly prevalent in a variety of fields, from
social network analysis to protein-protein interaction studies, growing concerns have …

Graph neural networks (GNNs) are models that capture the dependencies between graph
data by passing messages between graph nodes and they have been widely used to …