Graph neural networks (GNNs) have made rapid developments in the recent years. Due to
their great ability in modeling graph-structured data, GNNs are vastly used in various …

Adversarial attacks and defenses in machine learning and deep neural network (DNN) have
been gaining significant attention due to the rapidly growing applications of deep learning in …

The field of graph neural networks (GNNs) has seen rapid and incredible strides over the
recent years. Graph neural networks, also known as deep learning on graphs, graph …

Abstract Graph Neural Networks (GNNs) are increasingly important given their popularity
and the diversity of applications. Yet, existing studies of their vulnerability to adversarial …

Graph neural networks (GNNs) have emerged as a series of competent graph learning
methods for diverse real-world scenarios, ranging from daily applications like …

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 …

Adversarial Attacks and Defenses on Graphs Page 1 Adversarial Attacks and Defenses on
Graphs: A Review, A Tool and Empirical Studies Wei **†, Yaxin Li†, Han Xu†, Yiqi Wang† …

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 …

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