Graph Based Anomaly Detection Techniques: A Review

Abstract

Anomaly detection refers to the process of identifying anomalies or distinct patterns in data that might potentially point to issues. The motivation to study various anomaly detection techniques lies in the critical need to identify and respond to unusual patterns or deviations across multiple domains. There are various methods like statistical methods that are effective for data with known distributions, proximity-based and clustering-based methods excel in spatial data analysis, etc. Graph-based methods detect anomalies by disrupted connectivity patterns to enhance robustness and performance in diverse applications. In this article, some graph-based anomaly detection techniques have been studied in a nutshell and also some future directions to improve the technique of detecting anomalies in data have been given.