In recent years, data and computing resources are typically distributed in the devices of end
users, various regions or organizations. Because of laws or regulations, the distributed data …

Graph neural networks (GNNs) have attracted extensive research attention in recent years
due to their capability to progress with graph data and have been widely used in practical …

Graph neural networks (GNNs) have shown their superiority in modeling graph data. Owing
to the advantages of federated learning, federated graph learning (FGL) enables clients to …

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

Graph machine learning has gained great attention in both academia and industry recently.
Most of the graph machine learning models, such as Graph Neural Networks (GNNs), are …

Graph learning is a popular approach for perfor ming machine learning on graph-structured
data. It has revolutionized the machine learning ability to model graph data to address …

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 …

Vertical federated learning (VFL) is a promising category of federated learning for the
scenario where data is vertically partitioned and distributed among parties. VFL enriches the …

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 …

Graph Neural Networks (GNNs) have gained significant attention owing to their ability to
handle graph-structured data and the improvement in practical applications. However, many …