Graph based anomaly detection and description: a survey
TLDR
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, and gives a general framework for the algorithms categorized under various settings.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 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 versus (semi-)supervised approaches, for static versus dynamic graphs, for attributed versus 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.read more
Citations
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On the evaluation of unsupervised outlier detection: measures, datasets, and an empirical study
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A Unifying Review of Deep and Shallow Anomaly Detection
Lukas Ruff,Jacob R. Kauffmann,Robert A. Vandermeulen,Grégoire Montavon,Wojciech Samek,Marius Kloft,Thomas G. Dietterich,Klaus-Robert Müller +7 more
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Anomaly detection in dynamic networks: a survey
Stephen Ranshous,Stephen Ranshous,Shitian Shen,Shitian Shen,Danai Koutra,Steve Harenberg,Steve Harenberg,Christos Faloutsos,Nagiza F. Samatova,Nagiza F. Samatova +9 more
TL;DR: This work focuses on anomaly detection in static graphs, which do not change and are capable of representing only a single snapshot of data, but as real‐world networks are constantly changing, there has been a shift in focus to dynamic graphs,Which evolve over time.
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Community detection in networks: A multidisciplinary review
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TL;DR: A contemporary survey on the methods of community detection and its applications in the various domains of real life by reviewing prevailing community detection algorithms that range from traditional algorithms to state of the art algorithms for overlapping community detection.
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