Abstract: 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 traditional ...

Graph Neural Networks (GNNs) and network embedding techniques have emerged as powerful tools for analyzing and interpreting complex data structures represented as graphs. These methods are ...

To address the aforementioned challenges, this paper develops an FDIA detection approach that considers data falsification attacks on voltage regulation and enhances the voltage stability index. A ...

Colab Notebooks covering deep learning tools for biomolecular structure prediction and design ...

Backdoor attacks in neural networks represent a significant security threat in the field of machine learning. These attacks involve embedding hidden triggers into models during the training phase ...

MGMN consists of a node-graph matching network for effectively learning cross-level interactions between each node of one graph and the other whole graph, and a siamese graph neural network to learn ...

Scenario graphs used in the context of network security are called "attack graphs". Construction by hand of scenario graphs is tedious, error-prone, and impractical for graphs larger than a hundred ...

A new PayPal phishing attack has been confirmed with a critical twist: it’s phish-free. Here’s what you need to know.

By The Learning Network A new collection of graphs, maps and charts organized by topic and type from our “What’s Going On in This Graph?” feature. By The Learning Network Want to learn ...