# tegdet: An extensible Python Library for Anomaly Detection using Time-Evolving Graphs

arXiv: https://arxiv.org/abs/2210.08847 · HTML: https://mlsanity.com/paper/2210.08847/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 44 references that are in the map.
- First posted on arXiv 17 Oct 2022.

## Builds on (most cited first, 11 of 11)

- [A Comprehensive Survey on Graph Anomaly Detection with Deep Learning](https://mlsanity.com/paper/2106.07178/) (2021)
- [A review on outlier/anomaly detection in time series data](https://mlsanity.com/paper/2002.04236/) (2020)
- [Graph-based Anomaly Detection and Description: A Survey](https://mlsanity.com/paper/1404.4679/) (2014)
- [AnomalyDAE: Dual autoencoder for anomaly detection on attributed networks](https://mlsanity.com/paper/2002.03665/) (2020)
- [Anomaly Detection in Dynamic Graphs via Transformer](https://mlsanity.com/paper/2106.09876/) (2021)
- [Anomaly Detection in Univariate Time-series: A Survey on the State-of-the-Art](https://mlsanity.com/paper/2004.00433/) (2020)
- [Predicting Path Failure In Time-Evolving Graphs](https://mlsanity.com/paper/1905.03994/) (2019)
- [Graph Lifelong Learning: A Survey](https://mlsanity.com/paper/2202.10688/) (2022)
- [MIDAS: Microcluster-Based Detector of Anomalies in Edge Streams](https://mlsanity.com/paper/1911.04464/) (2019)
- [F-FADE: Frequency Factorization for Anomaly Detection in Edge Streams](https://mlsanity.com/paper/2011.04723/) (2020)
- [Fast and Accurate Anomaly Detection in Dynamic Graphs with a Two-Pronged Approach](https://mlsanity.com/paper/2011.13085/) (2020)

## Abstract (arXiv)

This paper presents a new Python library for anomaly detection in unsupervised learning approaches. The input for the library is a univariate time series representing observations of a given phenomenon. Then, it can identify anomalous epochs, i.e., time intervals where the observations are above a given percentile of a baseline distribution, defined by a dissimilarity metric. Using time-evolving graphs for the anomaly detection, the library leverages valuable information given by the inter-dependencies among data. Currently, the library implements 28 different dissimilarity metrics, and it has been designed to be easily extended with new ones. Through an API, the library exposes a complete functionality to carry out the anomaly detection. Summarizing, to the best of our knowledge, this library is the only one publicly available, that based on dynamic graphs, can be extended with other state-of-the-art anomaly detection techniques. Our experimentation shows promising results regarding the execution times of the algorithms and the accuracy of the implemented techniques. Additionally, the paper provides guidelines for setting the parameters of the detectors to improve their performance and prediction accuracy.