Recent years have witnessed fast developments of graph neural networks (GNNs) that have
benefited myriads of graph analytic tasks and applications. In general, most GNNs depend …

Abstract Graph Neural Networks (GNN) is one of the promising machine learning areas in
solving real world problems such as social networks, recommender systems, computer …

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

Although graph neural networks have exhibited remarkable performance in various graph
tasks, a significant concern is their vulnerability to adversarial attacks. Consequently, many …

Despite the success of graph neural networks (GNNs), their vulnerability to adversarial
attacks poses tremendous challenges for practical applications. Existing defense methods …

Recent studies have revealed that GNNs are vulnerable to adversarial attacks. To defend
against such attacks, robust graph structure refinement (GSR) methods aim at minimizing …

Modern graph neural networks (GNNs) can be sensitive to changes in the input graph
structure and node features, potentially resulting in unpredictable behavior and degraded …

Graph neural networks (GNNs) have emerged as a state-of-the-art approach to model and
draw inferences from large scale graph-structured data in various application settings such …

In this work, we rigorously investigate the robustness of graph neural fractional-order
differential equation (FDE) models. This framework extends beyond traditional graph neural …

This paper introduces a min-max optimization formulation for the Graph Signal Denoising
(GSD) problem. In this formulation, we first maximize the second term of GSD by introducing …