As graph representation learning often suffers from label scarcity problems in real-world
applications, researchers have proposed graph domain adaptation (GDA) as an effective …

In recent years, histopathological whole slide image (WSI)-based survival analysis has
attracted much attention in medical image analysis. In practice, WSIs usually come from …

In this study, we present a novel approach to adversarial attacks for graph neural networks
(GNNs), specifically addressing the unique challenges posed by graphical data. Unlike …

Spiking Graph Networks (SGNs) have garnered significant attraction from both researchers
and industry due to their ability to address energy consumption challenges in graph …

Graph Neural Networks (GNNs) with equivariant properties have achieved significant
success in modeling complex dynamic systems and molecular properties. However, their …

Graph neural networks (GNNs) have achieved impressive performance in graph domain
adaptation. However, extensive source graphs could be unavailable in real-world scenarios …

Deep learning models have been shown to outperform methods that rely on summary
statistics, like the power spectrum, in extracting information from complex cosmological data …

Graph neural networks (GNNs) have demonstrated their effectiveness in various tasks
supported by their generalization capabilities. However, the current analysis of GNN …

Semi-supervised graph domain adaptation, as a branch of graph transfer learning, aims to
annotate unlabeled target graph nodes by utilizing transferable knowledge learned from a …

Inferencing Gene Regulatory Networks (GRNs) from gene expression data is a pivotal
challenge in systems biology, and several innovative computational methods have been …