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
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TLDR
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.Abstract:
Anomaly detection is an important problem with multiple applications, and thus has been studied for decades in various research domains. In the past decade there has been a growing interest in anomaly detection in data represented as networks, or graphs, largely because of their robust expressiveness and their natural ability to represent complex relationships. Originally, techniques focused on anomaly detection in static graphs, which do not change and are capable of representing only a single snapshot of data. As real-world networks are constantly changing, there has been a shift in focus to dynamic graphs, which evolve over time.read more
Citations
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Continuous-Time Dynamic Network Embeddings
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A Unifying Review of Deep and Shallow Anomaly Detection
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TL;DR: This review aims to identify the common underlying principles and the assumptions that are often made implicitly by various methods in deep learning, and draws connections between classic “shallow” and novel deep approaches and shows how this relation might cross-fertilize or extend both directions.
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Progress in Outlier Detection Techniques: A Survey
TL;DR: This survey presents a comprehensive and organized review of the progress of outlier detection methods from 2000 to 2019 and categorizes them into different techniques from diverse outlier Detection techniques, such as distance-, clustering-, density-, ensemble-, and learning-based methods.
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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
TL;DR: Deep learning approaches to anomaly detection (AD) have recently improved the state of the art in detection performance on complex data sets, such as large collections of images or text as mentioned in this paper, and led to the introduction of a great variety of new methods.
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