Anomaly detection in dynamic networks: a survey

By Stephen Ranshous, Shitian Shen, Danai Koutra, Steve Harenberg, Christos Faloutsos, Nagiza F. Samatova. 2015

385 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Computer Networks and Communications, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/23f9220e0ca8f2cb522c36d158ca88f043007c68

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Hongzhi Wang
2. Mohamed Hammad
3. Mohamed Jaward Bah
4. Hui Xiong
5. Chuan Zhou
6. Jia Wu
7. Quan Z. Sheng
8. Wei Cheng

In Computer Networks and Communications

1. Hui Xiong
2. Wei Cheng
3. Mohamed Hammad
4. Jia Wu
5. Charu C. Aggarwal
6. Quan Z. Sheng
7. Chuan Zhou
8. Shan Xue

In Statistical and Nonlinear Physics

1. Hui Xiong
2. Wei Cheng
3. Mohamed Hammad
4. Jia Wu
5. Charu C. Aggarwal
6. Quan Z. Sheng
7. Chuan Zhou
8. Ryan A. Rossi

Citations in the corpus

• Progress in Outlier Detection Techniques: A Survey (2019, methodology): “OUTLIER DETECTION METHODS Outlier detection methods have been classified into different techniques such as statistical-based methods, distancebased methods, graphical-based methods, geometric-based methods, depth-based methods, profiling methods, modelbased and density-based methods in a wide range ”
• Continuous-Time Dynamic Network Embeddings (2018, background): “and dynamic mixed-membership role models [22, 59], anomaly detection in dynamic networks [55], influence modeling and online advertisement [24], finding important entities in dynamic networks [23, 49] temporal network centrality and measures [29].”
• NetWalk: A Flexible Deep Embedding Approach for Anomaly Detection in Dynamic Networks (2018, methodology): “Anomaly detection (a.k.a outlier detection) in dynamically changing networks is a long-standing problem deeply motivated in a number of application domains, such as social media, security, public health, and computational biology [1, 5, 7, 16, 33].”
• A Comprehensive Survey on Graph Anomaly Detection With Deep Learning (2021, methodology): “As for graph anomaly detection, Akoglu et al. [25], Ran-shous et al. [48], and Jennifer and Kumar [49] put their concentration on graph anomaly detection, reviewing many conventional approaches in this area, including statistical models and machine learning techniques.”