This paper investigates the problem of learning privacy-preserving graph representations
with graph neural networks (GNNs). Different from existing works based on adversarial …

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

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 become a popular tool for learning on graphs, but their
widespread use raises privacy concerns as graph data can contain personal or sensitive …

Recently, Graph Neural Network (GNN) has achieved remarkable progresses in various real-
world tasks on graph data, consisting of node features and the adjacent information between …

Graph Neural Networks (GNNs) as an emerging technique have shown excellent
performance in a variety of fields, such as social networks and recommendation systems …

With the increasing popularity of graph neural networks (GNNs) in several sensitive
applications like healthcare and medicine, concerns have been raised over the privacy …

In graph machine learning, data collection, sharing, and analysis often involve multiple
parties, each of which may require varying levels of data security and privacy. To this end …

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 …

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