Graph Neural Networks (GNNs) have exploded onto the machine learning scene in recent
years owing to their capability to model and learn from graph-structured data. Such an ability …

Graph neural networks (GNNs) are a type of deep learning models that are trained on
graphs and have been successfully applied in various domains. Despite the effectiveness of …

As explanations are increasingly used to understand the behavior of graph neural networks
(GNNs), evaluating the quality and reliability of GNN explanations is crucial. However …

Graph neural networks (GNNs) are among the most powerful tools in deep learning. They
routinely solve complex problems on unstructured networks, such as node classification …

Fully Homomorphic Encryption (FHE) enables the processing of encrypted data without
decrypting it. FHE has garnered significant attention over the past decade as it supports …

Homomorphic Encryption (HE) enables users to securely outsource both the storage and
computation of sensitive data to untrusted servers. Not only does HE offer an attractive …

In the acceleration of deep neural network training, the graphics processing unit (GPU) has
become the mainstream platform. GPUs face substantial challenges on Graph Neural …

In the acceleration of deep neural network training, the graphics processing unit (GPU) has
become the mainstream platform. GPUs face substantial challenges on Graph Neural …

Graph neural networks (GNNs) suffer from low GPU utilization due to frequent memory
accesses. Existing concurrent training mechanisms cannot be directly adapted to GNNs …

Heterogeneous graph neural networks (HGNNs) have emerged as powerful algorithms for
processing heterogeneous graphs (HetGs), widely used in many critical fields. To capture …