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) have emerged as a series of competent graph learning
methods for diverse real-world scenarios, ranging from daily applications such as …

Detecting which nodes in graphs are outliers is a relatively new machine learning task with
numerous applications. Despite the proliferation of algorithms developed in recent years for …

A cursory reading of the literature suggests that we have made a lot of progress in designing
effective adversarial defenses for Graph Neural Networks (GNNs). Yet, the standard …

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

Deep neural networks (DNNs) have been widely applied to various applications, including
image classification, text generation, audio recognition, and graph data analysis. However …

Despite the impressive performance of Artificial Intelligence (AI) systems, their robustness
remains elusive and constitutes a key issue that impedes large-scale adoption. Besides …

Despite its success in the image domain, adversarial training did not (yet) stand out as an
effective defense for Graph Neural Networks (GNNs) against graph structure perturbations …

Abstract Graph Neural Networks (GNNs) have received extensive research attention for their
promising performance in graph machine learning. Despite their extraordinary predictive …

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 …