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...
Through minimizing the sample energy, we maximize the likelihood of non-anomalous samples, and predict samples with top-K high energy as anomalies。这一部分套了一个高斯混合模型,看得我一脸问号,索性贴段原文。 4. ALARM A deep multi-view framework for anomaly detection on attributed networks ALARM...
Graph Anomaly Detection with Graph Neural Networks: Current Status and Challenges Hwan Kim, Byung Suk Lee, Won-Yong Shin, Senior Member, IEEE, and Sungsu Lim, Member, IEEE Weakly Supervised Anomaly Detection: A Survey Minqi Jiang,Chaochuan Hou,Ao Zheng,Xiyang Hu,Songqiao Han,Hailiang Huang,...
ANOMALY detection (Computer security)CONVOLUTIONAL neural networksDEEP learningGRAPH theoryARTIFICIAL neural networksAnomaly detection in network data is a critical task in various domains, and graph-based approaches, particularly Graph Convolutional Networks (GCNs), have gained significant...
1.介绍 现代高科技系统,如云服务器或高性能计算机,通常由大量的组件组成。随着时间的推移,这些系统变得越来越复杂,使得手动系统操作和维护变得困难甚至不可行[17]。因此,自动化系统操作和维护是非常可取的。为了实现这一点,系统日志被普遍用于记录系统状态和重要事件
This review explores the role of Graph Neural Networks (GNNs) in addressing these challenges by proposing a unified framework that categorizes existing GNN methodologies applied to financial fraud detection. Specifically, by examining a series of detailed research questions, this review delves into the...
Motivated by the demand for wind turbines anomaly detection, the stronger representation capability of graph data, and the fast development of unsupervised graph neural networks, this work presents an anomaly detection framework for wind turbines using SCADA data based on physical-statistical feature fusi...
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)....
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 Networks for Anomaly Detection in...
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...