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

The heterogeneous information network (HIN), which contains rich semantics depicted by
meta-paths, has emerged as a potent tool for mitigating data sparsity in recommender …

Building a graph neural network (GNN)-based recommender system without violating user
privacy proves challenging. Existing methods can be divided into federated GNNs and …

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

Graph neural networks (GNNs) can learn the node representations to capture both node
features and graph topology information through the message passing mechanism …

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 (GNN) are widely used in recommendation systems, but traditional
centralized methods raise privacy concerns. To address this, we introduce a federated …

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

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 …

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