SpaceGNN: Multi-Space Graph Neural Network for Node Anomaly Detection …
Feb 5, 2025 · Abstract: Node Anomaly Detection (NAD) has gained significant attention in the deep learning community due to its diverse applications in real-world scenarios. Existing NAD …
Anomaly Detection using Graph Neural Networks - IEEE Xplore
In this paper, we utilize the ability of Deep Learning over topological characteristics of a social network to detect anomalies in email network and twitter network. We present a model, Graph …
Graph Neural Networks in Anomaly Detection | SpringerLink
Jan 3, 2022 · Recently, graph neural networks (GNNs), as a powerful deep-learning-based graph representation technique, has demonstrated superiority in leveraging the graph structure and …
Graph neural network approach for anomaly detection
Aug 1, 2021 · To ensure the stable long-time operation of satellites, evaluate the satellite status, and improve satellite maintenance efficiency, we propose an anomaly detection method based …
Graph Neural Networks(GNNs) for Anomaly Detection with …
Feb 18, 2024 · Graph Neural Networks (GNNs) are a type of deep learning model that can learn from graph-structured data, such as social networks, citation networks, or molecular graphs. …
We formulate a GNN-based network anomaly detection technique that eficiently manages escalating network data volumes and mitigates vulnerabilities, fortifying against potential …
Improving Robustness of GNN-based Anomaly Detection by Graph ...
3 days ago · To address this critical challenge, we introduce an innovative mechanism for graph adversarial training, meticulously designed to bolster GNN-based anomaly detection systems …
[2209.14930] Graph Anomaly Detection with Graph Neural Networks ...
Sep 29, 2022 · In this survey, we review the recent advances made in detecting graph anomalies using GNN models. Specifically, we summarize GNN-based methods according to the graph …
(PDF) Graph Anomaly Detection With Graph Neural Networks: …
Jan 1, 2022 · To solve the graph anomaly detection problem, GNN-based methods leverage information about the graph attributes (or features) and/or structures to learn to score …
Dual-discriminative Graph Neural Network for Imbalanced Graph …
Abstract. Graph-level anomaly detection aims to distinguish anomalous graphs in a graph dataset from normal graphs. Anomalous graphs represent a very few but essential patterns in the real …
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