Graph-based Anomaly Detection and Description: A Survey

TL;DR

This survey comprehensively reviews state-of-the-art graph-based anomaly detection methods, categorizing algorithms by settings, emphasizing explainability, and discussing real-world applications and future challenges.

Contribution

It provides a structured overview of graph anomaly detection techniques, including data mining and machine learning approaches, with a focus on explainability and diverse application domains.

Findings

Effective methods highlighted for static and dynamic graphs

Scalability and robustness of techniques discussed

Applications in finance, social networks, and traffic analyzed

Abstract

Detecting anomalies in data is a vital task, with numerous high-impact applications in areas such as security, finance, health care, and law enforcement. While numerous techniques have been developed in past years for spotting outliers and anomalies in unstructured collections of multi-dimensional points, with graph data becoming ubiquitous, techniques for structured {\em graph} data have been of focus recently. As objects in graphs have long-range correlations, a suite of novel technology has been developed for anomaly detection in graph data. This survey aims to provide a general, comprehensive, and structured overview of the state-of-the-art methods for anomaly detection in data represented as graphs. As a key contribution, we provide a comprehensive exploration of both data mining and machine learning algorithms for these {\em detection} tasks. we give a general framework for the algorithms categorized under various settings: unsupervised vs. (semi-)supervised approaches, for static vs. dynamic graphs, for attributed vs. plain graphs. We highlight the effectiveness, scalability, generality, and robustness aspects of the methods. What is more, we stress the importance of anomaly {\em attribution} and highlight the major techniques that facilitate digging out the root cause, or the `why', of the detected anomalies for further analysis and sense-making. Finally, we present several real-world applications of graph-based anomaly detection in diverse domains, including financial, auction, computer traffic, and social networks. We conclude our survey with a discussion on open theoretical and practical challenges in the field.