Anomaly Detection in Graph Structured Data: A Survey
This is an incremental survey paper that organizes existing knowledge for researchers working on anomaly detection in graph data.
This paper provides a comprehensive survey of anomaly detection techniques for graph-structured data, presenting a new taxonomy to categorize state-of-the-art methods and discussing their applications, advantages, disadvantages, and future research directions.
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.