What Are Anomalies in a Network?

This article examines a collection of assumptions used in the current literature on node anomaly detection in a network. The examination raises the question: What are anomalies in a network? Our attempt to answer this question has provided some interesting findings and led to some open questions. This is the first article which formally defines anomalies in a network and introduces the concept of self-verifiability of a detector without ground-truths in a network. They enable existing detectors to be categorized into two types along the line whether they are self-verifiable or not. We suggest a method to evaluate self-verifiable detectors without ground-truths as an alternative to the existing evaluation method that relies on ground-truths.

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Publication Details

Journal
ACM Transactions on Knowledge Discovery from Data
Published
2025-05-27
DOI
https://doi.org/10.1145/3723007
Primary Topic
Anomaly Detection Techniques and Applications
Type
article
Field-Weighted Citation Impact
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article

What Are Anomalies in a Network?

Kai Ming Ting, Guansong Pang, Zongyou Liu, Zhong Zhuang et al.
ACM Transactions on Knowledge Discovery from Data
Anomaly Detection Techniques and Applications
article

What Are Anomalies in a Network?

Kai Ming Ting, Guansong Pang, Zongyou Liu, Zhong Zhuang, Qiujun Zhao, Tang Liang
article en

Abstract

This article examines a collection of assumptions used in the current literature on node anomaly detection in a network. The examination raises the question: What are anomalies in a network? Our attempt to answer this question has provided some interesting findings and led to some open questions. This is the first article which formally defines anomalies in a network and introduces the concept of self-verifiability of a detector without ground-truths in a network. They enable existing detectors to be categorized into two types along the line whether they are self-verifiable or not. We suggest a method to evaluate self-verifiable detectors without ground-truths as an alternative to the existing evaluation method that relies on ground-truths.

ACM Transactions on Knowledge Discovery from DataVol. 19(6)
Singapore Management University (SG), Nanjing University (CN)
Openalex Percentile: Top 95%
Anomaly Detection Techniques and Applications
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