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...
3.3. Beta Wavelet Graph Neural Network 4. 实验 4.1. 实验设置 4.2. 性能比较 4.3. 敏感性分析 5. 结论 论文出处:ICML 2022 论文地址:arxiv.org/pdf/2205.1550 代码地址:GitHub - squareRoot3/Rethinking-Anomaly-Detection: "Rethinking Graph Neural Networks for Anomaly Detection" in ICML 2022 摘要 图神...
Anomaly Detection in Dynamic Graphs Dynamic graphs, where the structure evolves, present unique challenges for anomaly detection. NECLA’s research in this area has led to the development of Structural Temporal Graph Neural Networks (StrGNN). Detailed in the publication“Structural Temporal Graph Neural...
Kim H, Lee B S, Shin W Y, et al. Graph Anomaly Detection with Graph Neural Networks: Current Status and Challenges[J]. IEEE Access, 2022. 团队主要是韩国的 IEEE Access,h-index:56, CiteScore:6.70 Abstract 图异常:是指图中不符合正常模式的图形属性或结构的模式。 解决方法:基于GNN的方法利用...
This example shows how to detect anomalies in multivariate time series data using a graph neural network (GNN).
GNNs are well suited to making use of the highly relational nature of LHC data through mechanisms such as neural message passing. GNNs have been applied to various LHC physics tasks including reconstruction (clustering), identification (classification), calibration (regression), anomaly detection and ...
E-GraphSAGE: A Graph Neural Network based Intrusion Detection System 介绍 翻译 训练阶段 GNN GraphSAGE Forward Propagation - Node Embedding 重要文献 介绍 总之,本文的主要贡献有两个: • 我们提出并实现了 E-GraphSAGE,它是 GraphSAGE 的扩展,它允许结合边缘特征/属性进行图表示学习。 这一贡献适用于一系列...
A survey of anomaly detection techniques in financial domain. Future Generation Computer Systems, 2016, 55: 278–288 Article MATH Google Scholar Cheng D, Wang X, Zhang Y, Zhang L. Graph neural network for fraud detection via spatial-temporal attention. IEEE Transactions on Knowledge and Data ...
we propose one-class graph neural network (OCGNN), a one-class classification framework for graph anomaly detection. OCGNN is designed to combine the powerful representation ability of graph neural networks along with the classical one-class objective. Compared with other baselines, OCGNN achieves ...
Graph Neural NetworkGraph Neural NetworkGraph LearningGraph LearningNode ClassificationNode ClassificationPredictionPredictionGraph AttentionGraph AttentionGraph ClassificationGraph ClassificationAnomaly DetectionAnomaly DetectionOtherOther TaskPapersShare Graph Neural Network 500 40.03% Graph...