Machine learning (ML) models have been widely applied to various applications, including
image classification, text generation, audio recognition, and graph data analysis. However …

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 …

Contrastive learning has recently attracted plenty of attention in deep graph clustering due to
its promising performance. However, complicated data augmentations and time-consuming …

Graph neural networks (GNNs) have emerged as a series of competent graph learning
methods for diverse real-world scenarios, ranging from daily applications like …

Deploying machine learning models in production may allow adversaries to infer sensitive
information about training data. There is a vast literature analyzing different types of …

Text-to-image generation models have recently attracted unprecedented attention as they
unlatch imaginative applications in all areas of life. However, developing such models …

While graph neural networks (GNNs) dominate the state-of-the-art for exploring graphs in
real-world applications, they have been shown to be vulnerable to a growing number of …

In this paper, we study the problem of learning Graph Neural Networks (GNNs) with
Differential Privacy (DP). We propose a novel differentially private GNN based on …

Machine learning models are shown to face a severe threat from Model Extraction Attacks,
where a well-trained private model owned by a service provider can be stolen by an attacker …

Graph Neural Networks (GNNs) have been increasingly deployed in a plethora of
applications. However, the graph data used for training may contain sensitive personal …