Аннотация
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 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 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 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.
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