Graph structured data have enabled several successful applications such as
recommendation systems and traffic prediction, given the rich node features and edges …

As machine learning becomes more widely used for critical applications, the need to study
its implications in privacy turns to be urgent. Given access to the target model and auxiliary …

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 …

Graph Neural Networks (GNNs) are a popular technique for modelling graph-structured data
and computing node-level representations via aggregation of information from the …

Graph Neural Networks (GNNs) have demonstrated superior performance in learning node
representations for various graph inference tasks. However, learning over graph data can …

Abstract Graph Neural Networks (GNNs) have proven to be highly effective in solving real-
world learning problems that involve graph-structured data. However, GNNs can also …

Classification tasks on labeled graph-structured data have many important applications
ranging from social recommendation to financial modeling. Deep neural networks are …

Recent studies have shown Graph Neural Networks (GNNs) are extremely vulnerable to
attribute inference attacks. To tackle this challenge, existing privacy-preserving GNNs …

Social networks are considered to be heterogeneous graph neural networks (HGNNs) with
deep learning technological advances. HGNNs, compared to homogeneous data, absorb …

Many real-world networks are inherently decentralized. For example, in social networks,
each user maintains a local view of a social graph, such as a list of friends and her profile. It …