Anomaly Detection in Graph Structured Data: A Survey
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.
Code (0)
등록된 구현이 없습니다.
Tasks
Anomaly DetectionSurveySimilar Papers 제목 키워드 기반
A Survey of Heterogeneous Graph Neural Networks for Cybersecurity Anomaly Detection
Anomaly detection is a critical task in cybersecurity, where identifying insider threats, access violations, and coordinated attacks is essential for ensuring system resilience. Graph-based approaches have become increas…
Anomaly DetectionAnomaly Detection in Dynamic Graphs: A Comprehensive Survey
This survey paper presents a comprehensive and conceptual overview of anomaly detection using dynamic graphs. We focus on existing graph-based anomaly detection (AD) techniques and their applications to dynamic networks.…
Anomaly DetectionGraph MiningSurveyA Survey on Explainable Anomaly Detection
In the past two decades, most research on anomaly detection has focused on improving the accuracy of the detection, while largely ignoring the explainability of the corresponding methods and thus leaving the explanation …
Anomaly DetectionSurveyA Comprehensive Survey on Graph Anomaly Detection with Deep Learning
Anomalies represent rare observations (e.g., data records or events) that deviate significantly from others. Over several decades, research on anomaly mining has received increasing interests due to the implications of t…
Anomaly DetectionDeep LearningGraph Anomaly DetectionSurveyDeep Learning for Time Series Anomaly Detection: A Survey
Time series anomaly detection has applications in a wide range of research fields and applications, including manufacturing and healthcare. The presence of anomalies can indicate novel or unexpected events, such as produ…
Anomaly DetectionDeep LearningSurveyTime Series+2