Anomaly Detection in Graph Structured Data: A Survey

TL;DR

This survey provides a comprehensive overview of anomaly detection techniques in graph-structured data, categorizing methods, analyzing their strengths and weaknesses, and suggesting future research directions.

Contribution

It introduces a new taxonomy for classifying graph anomaly detection methods based on assumptions and techniques, and discusses their fundamental research ideas.

Findings

Categorized existing anomaly detection methods using a new taxonomy.

Analyzed advantages and disadvantages of current techniques.

Outlined future research directions in graph anomaly detection.

Abstract

Real-world graphs are complex to process for performing effective analysis, such as anomaly detection. However, recently, there have been several research efforts addressing the issues surrounding graph-based anomaly detection. In this paper, we discuss a comprehensive overview of anomaly detection techniques on graph data. We also discuss the various application domains which use those anomaly detection techniques. We present a new taxonomy that categorizes the different state-of-the-art anomaly detection methods based on assumptions and techniques. Within each category, we discuss the fundamental research ideas that have been done to improve anomaly detection. We further discuss the advantages and disadvantages of current anomaly detection techniques. Finally, we present potential future research directions in anomaly detection on graph-structured data.