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的方法利用关于图形属性(或特征)和/或结构的信息来学习...
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,X...
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...
This article is a detailed technical deep dive into how to build a powerful model for anomaly detection with graph data containing entities of different types (heterogeneous graph data). The model…
[1] A. Deng and B. Hooi, “Graph neural network-based anomaly detection in multivariate time series,” in Proceedings of the 35th AAAI Conference on Artificial Intelligence, 2021. See Also How to Get Best Site Performance Select the China site (in Chinese or English) for best ...
Unsupervised anomaly detection using graph neural networks integrated with physical-statistical feature fusion and local-global learning Efficient and feasible anomaly detection scheme that could utilize data collected by supervisory-control-anddata-acquisition (SCADA) system is essential fo... C Feng,C Li...
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 on graph neural network and dynamic threshold (GNN-DTAN). Firstly, we build the graph neural network model for telemetry ...
内容提示: Graph Neural Network-Based Anomaly Detection in Multivariate Time SeriesAilin Deng, Bryan HooiNational University of Singaporeailin@comp.nus.edu.sg, bhooi@comp.nus.edu.sgAbstractGiven high-dimensional time series data (e.g., sensor data),how can we detect anomalous events, such as ...
“Addressing Heterophily in Graph Anomaly Detection：A Perspective of Graph Spectrum”该方法能够有效...