Fully Homomorphic Encryption (FHE) offers protection to private data on third-party cloud
servers by allowing computations on the data in encrypted form. To support general-purpose …

Characterizing graph neural networks (GNNs) is essential for identifying performance
bottlenecks and facilitating their deployment. Despite substantial work in this area, a …

The wide adoption of Cloud Native shifts the boundary between cloud users and CSPs
(Cloud Service Providers) from VM-based infrastructure to container-based applications …

Dynamic Graph Neural Networks (DGNNs) have been widely applied in various real-life
applications, such as link prediction and pandemic forecast, to capture both static structural …

Embedding retrieval is a crucial task for recommendation systems. Graph-based
approximate nearest neighbor search (GANNS) is the most commonly used method for …

In recent times, a plethora of hardware accelerators have been put forth for graph learning
applications such as vertex classification and graph classification. However, previous works …

With the development of loT and edge computing, data analytics on edge has become
popular, and text analytics directly on compression (TADOC) has been proven to be a …

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

As the size of real-world graphs increases, training Graph Neural Networks (GNNs) has
become time-consuming and requires acceleration. While previous works have …

Graph neural networks (GNN) one of the most popular neural network models, are
extensively applied in graph-related fields, including drug discovery, recommendation …