# Anomaly Mining -- Past, Present and Future

arXiv: https://arxiv.org/abs/2105.10077 · HTML: https://mlsanity.com/paper/2105.10077/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 61 references that are in the map.
- First posted on arXiv 21 May 2021.

## Builds on (most cited first, 15 of 15)

- [A Unifying Review of Deep and Shallow Anomaly Detection](https://mlsanity.com/paper/2009.11732/) (2020)
- [Deep Learning for Anomaly Detection: A Survey](https://mlsanity.com/paper/1901.03407/) (2019)
- [Graph-based Anomaly Detection and Description: A Survey](https://mlsanity.com/paper/1404.4679/) (2014)
- [Current Time Series Anomaly Detection Benchmarks are Flawed and are Creating the Illusion of Progress](https://mlsanity.com/paper/2009.13807/) (2020)
- [Causal structure based root cause analysis of outliers](https://mlsanity.com/paper/1912.02724/) (2019)
- [Few-shot Network Anomaly Detection via Cross-network Meta-learning](https://mlsanity.com/paper/2102.11165/) (2021)
- [On Using Classification Datasets to Evaluate Graph-Level Outlier Detection: Peculiar Observations and New Insights](https://mlsanity.com/paper/2012.12931/) (2020)
- [A Meta-Analysis of the Anomaly Detection Problem](https://mlsanity.com/paper/1503.01158/) (2015)
- [Contextual Outlier Interpretation](https://mlsanity.com/paper/1711.10589/) (2017)
- [Explaining Anomalies in Groups with Characterizing Subspace Rules](https://mlsanity.com/paper/1708.05929/) (2017)
- [Automating Outlier Detection via Meta-Learning](https://mlsanity.com/paper/2009.10606/) (2020)
- [Spotting Suspicious Link Behavior with fBox: An Adversarial Perspective](https://mlsanity.com/paper/1410.3915/) (2014)
- [One-Class Graph Neural Networks for Anomaly Detection in Attributed Networks](https://mlsanity.com/paper/2002.09594/) (2020)
- [Anomaly Detection in Large Labeled Multi-Graph Databases](https://mlsanity.com/paper/2010.03600/) (2020)
- [AutoOD: Automated Outlier Detection via Curiosity-guided Search and Self-imitation Learning](https://mlsanity.com/paper/2006.11321/) (2020)

## Abstract (arXiv)

Anomaly mining is an important problem that finds numerous applications in various real world domains such as environmental monitoring, cybersecurity, finance, healthcare and medicine, to name a few. In this article, I focus on two areas, (1) point-cloud and (2) graph-based anomaly mining. I aim to present a broad view of each area, and discuss classes of main research problems, recent trends and future directions. I conclude with key take-aways and overarching open problems.