Graphs naturally appear in numerous application domains, ranging from social analysis,
bioinformatics to computer vision. The unique capability of graphs enables capturing the …

Graph neural networks provide a powerful toolkit for embedding real-world graphs into low-
dimensional spaces according to specific tasks. Up to now, there have been several surveys …

Graph neural networks have-enabled the application of deep learning to problems that can
be described by graphs, which are found throughout the different fields of sci-ence, from …

Existing knowledge distillation methods focus on convolutional neural networks (CNNs),
where the input samples like images lie in a grid domain, and have largely overlooked …

Two architectures that generalize convolutional neural networks (CNNs) for the processing
of signals supported on graphs are introduced. We start with the selection graph neural …

Graph neural networks (GNNs) are vulnerable to adversarial perturbations, including those
that affect both node features and graph topology. This paper investigates GNNs derived …

In this study, we introduce an interpretable graph-based deep learning prediction model,
AttentionSiteDTI, which utilizes protein binding sites along with a self-attention mechanism …

Neural diffusion on graphs is a novel class of graph neural networks that has attracted
increasing attention recently. The capability of graph neural partial differential equations …

Contrastive learning has been successfully applied in unsupervised representation learning.
However, the generalization ability of representation learning is limited by the fact that the …

Graph Neural Networks (GNNs) have achieved promising performance in various real-world
applications. However, recent studies have shown that GNNs are vulnerable to adversarial …