When viewed as a graph, a network of computers can be analyzed with GNNs for anomaly detection. Anomalies within provenance graphs often correlate to malicious activity within the network.

Nov 27, 2025 · Graph Neural Networks work by allowing nodes in a graph to share information with their neighbors through a process known as message passing. Since graphs are irregular and …

Jul 21, 2022 · Learn everything about Graph Neural Networks, including what GNNs are, the different types of graph neural networks, and what they're used for. Plus, learn how to build a Graph Neural …

What is a GNN (graph neural network)? Graph neural networks (GNNs) are a deep neural network architecture that is popular both in practical applications and cutting-edge machine learning research. …

Sep 2, 2021 · A set of objects, and the connections between them, are naturally expressed as a graph. Researchers have developed neural networks that operate on graph data (called graph neural …

Feb 1, 2022 · In this article, I help you get started and understand how graph neural networks work while also trying to address the question "why" at each stage. Finally we will also take a look at …

Jan 1, 2020 · Graph neural networks (GNNs) are deep learning based methods that operate on graph domain. Due to its convincing performance, GNN has become a widely applied graph analysis …

Jan 16, 2024 · Graph neural networks (GNNs) are a type of deep learning model that can be used to learn from graph data. GNNs use a message-passing mechanism to aggregate information from …

Graph neural networks (GNNs) have recently grown in popularity in the field of artificial intelligence (AI) due to their unique ability to ingest relatively unstructured data types as input data.

Jul 31, 2025 · Graph Neural Network (GNN) is a new model that can be used to analyse graphs. Graphs are robust data structures that contain relationships between objects, and GNNs allow you to explore …