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 …

Differentially private GNNs (Graph Neural Networks) have been recently studied to provide
high accuracy in various tasks on graph data while strongly protecting user privacy. In …

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 …

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 …

In this work, we study the applications of differential privacy (DP) in the context of graph-
structured data. We discuss the formulations of DP applicable to the publication of graphs …

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

Graph Neural Networks (GNNs) have established themselves as the state-of-the-art models
for many machine learning applications such as the analysis of social networks, protein …

Currently, the federated graph neural network (GNN) has attracted a lot of attention due to its
wide applications in reality without violating the privacy regulations. Among all the privacy …

Learning with graphs has attracted significant attention recently. Existing representation
learning methods on graphs have achieved state-of-the-art performance on various graph …