Anomaly Detection in Dynamic Graphs: A Comprehensive Survey

TL;DR

This comprehensive survey reviews various techniques for anomaly detection in dynamic graphs, categorizing approaches, discussing their advantages, and highlighting future research challenges in the field.

Contribution

It introduces a new review framework for dynamic graph anomaly detection approaches and compares existing surveys, providing insights into strengths, limitations, and future directions.

Findings

Deep learning approaches show promising results.

Graph-based methods effectively capture relational structures.

Identified key challenges and open research questions.

Abstract

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. The contributions of this survey paper include the following: i) a comparative study of existing surveys on anomaly detection; ii) a Dynamic Graph-based Anomaly Detection (DGAD) review framework in which approaches for detecting anomalies in dynamic graphs are grouped based on traditional machine-learning models, matrix transformations, probabilistic approaches, and deep-learning approaches; iii) a discussion of graphically representing both discrete and dynamic networks; and iv) a discussion of the advantages of graph-based techniques for capturing the relational structure and complex interactions in dynamic graph data. Finally, this work identifies the potential challenges and future directions for detecting anomalies in dynamic networks. This DGAD survey approach aims to provide a valuable resource for researchers and practitioners by summarizing the strengths and limitations of each approach, highlighting current research trends, and identifying open challenges. In doing so, it can guide future research efforts and promote advancements in anomaly detection in dynamic graphs. Keywords: Graphs, Anomaly Detection, dynamic networks,Graph Neural Networks (GNN), Node anomaly, Graph mining.