GitHub

Graph Neural Network-Based Anomaly Detection (GNNAD)

gnnad is a package for anomaly detection on multivariate time series data. This model builds on the recently-proposed Graph Deviation Network (GDN) 1, a graph neural network model that uses embeddings ...
IEEE

A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detection

Abstract: Time series are the primary data type used to record dynamic system measurements and generated in great volume by both physical sensors and online processes (virtual sensors). Time series ...
IEEE

Learning to Detect Critical Nodes in Sparse Graphs via Feature Importance Awareness

Abstract: Detecting critical nodes in sparse graphs is important in a variety of application domains, such as network vulnerability assessment, epidemic control, and drug design. The critical node ...