一是不同传感器之间有着非常不同的行为,即图中节点的数值和分布差异很大,因此需要考虑如何对传感器,即图中节点进行特征表示;二是GNNs的输入必须是整个图,即包括图中节点的特征表示以及各节点的连接关系,而在本文场景中,各节点之间的关系都是未知的(以往的方法是直接认为各节点之间都存在关系,即使用完全图表征各节点的关系),也就是各节点
Anomaly detectionTo ensure the stable long-time operation of satellites, evaluate the satellite status, and improve satellite maintenance efficiency, we propose an anomaly detection method based on graph neural network and dynamic threshold (GNN-DTAN). Firstly, we build the graph neural network model...
回顾了异常检测(Anomaly Detection)、多元时间序列数据模型 (models for multivariate time series data)、图神经网络(Graph neural network)的研究相关工作,并指出其不足。 2.1 异常检测(Anomaly Detection) 目的是检测出偏离大部分数据的异常样本,经典方法包括基于密度的研究方法、基于线性模型的研究方法、基于距离的研究...
Xiao et al. [11] proposed a graph embedding approach to perform anomaly detection on network flows. The authors first converted the network flows into a first-order and secondorder graph. The first-order graph learns the latent features from the perspective of a single host by using its IP ...
Multivariate Time Series Anomaly Detection Using Graph Neural Network This example uses: Deep Learning Toolbox Statistics and Machine Learning Toolbox Copy Code Copy CommandThis example shows how to detect anomalies in multivariate time series data using a graph neural network (GNN)....
Code implementation for :Graph Neural Network-Based Anomaly Detection in Multivariate Time Series(AAAI'21) Installation Python >= 3.6 cuda == 10.2 Pytorch==1.5.1 PyG: torch-geometric==1.5.0 Install packages # run after installing correct Pytorch package bash install.sh ...
For industrial big data, anomaly detection for multivariate time series data is of critical strategic significance. However, the complexity of industrial e
Point-GNN: graph neural network for 3D object detection in a point cloud. Preprint at https://arxiv.org/abs/2003.01251 (2020). Albertsson, K. & Meloni, F. Displaced event classification using graph networks. In Connecting the Dots Workshop 2020 (CTD2020) (Zenodo, 2020); https://doi....
Xiao et al. [11] proposed a graph embedding approach to perform anomaly detection on network flows. The authors first converted the network flows into a first-order and secondorder graph. The first-order graph learns the latent features from the perspective of a single host by using its IP ...
ASA-GNN: adaptive sampling and aggregation-based graph neural network for transaction fraud detection. IEEE Transactions on Computational Social Systems, 2024, 11(3): 3536–3549 Article MATH Google Scholar Sun H, Liu Z, Wang S, Wang H. Adaptive attention-based graph representation learning to ...