# Machine Learning Partners in Criminal Networks

arXiv: https://arxiv.org/abs/2209.03171 · HTML: https://mlsanity.com/paper/2209.03171/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 49 references that are in the map.
- First posted on arXiv 7 Sep 2022.

## Builds on (most cited first, 16 of 16)

- [node2vec: Scalable Feature Learning for Networks](https://mlsanity.com/paper/1607.00653/) (2016)
- [DeepWalk: Online Learning of Social Representations](https://mlsanity.com/paper/1403.6652/) (2014)
- [LINE: Large-scale Information Network Embedding](https://mlsanity.com/paper/1503.03578/) (2015)
- [A Comprehensive Survey of Graph Embedding: Problems, Techniques and Applications](https://mlsanity.com/paper/1709.07604/) (2017)
- [Machine Learning on Graphs: A Model and Comprehensive Taxonomy](https://mlsanity.com/paper/2005.03675/) (2020)
- [Interpretable Machine Learning -- A Brief History, State-of-the-Art and Challenges](https://mlsanity.com/paper/2010.09337/) (2020)
- [Network Representation Learning: A Survey](https://mlsanity.com/paper/1801.05852/) (2017)
- [A Survey on Graph Representation Learning Methods](https://mlsanity.com/paper/2204.01855/) (2022)
- [A Survey of Explainable Graph Neural Networks: Taxonomy and Evaluation Metrics](https://mlsanity.com/paper/2207.12599/) (2022)
- [Social physics](https://mlsanity.com/paper/2110.01866/) (2021)
- [Mercator: uncovering faithful hyperbolic embeddings of complex networks](https://mlsanity.com/paper/1904.10814/) (2019)
- [Corruption Risk in Contracting Markets: A Network Science Perspective](https://mlsanity.com/paper/1909.08664/) (2019)
- [A network approach to cartel detection in public auction markets](https://mlsanity.com/paper/1906.08667/) (2019)
- [The dynamical structure of political corruption networks](https://mlsanity.com/paper/1801.01869/) (2018)
- [Statistical physics of crime: A review](https://mlsanity.com/paper/1411.1743/) (2014)
- [Complexity science approach to economic crime](https://mlsanity.com/paper/2008.12364/) (2020)

## Built on by (most cited first, 4 of 4)

- [Easy attention: A simple attention mechanism for temporal predictions with transformers](https://mlsanity.com/paper/2308.12874/) (2023)
- [Enhancing Imbalance Learning: A Novel Slack-Factor Fuzzy SVM Approach](https://mlsanity.com/paper/2411.17128/) (2024)
- [Machine learning approach to detect dynamical states from recurrence measures](https://mlsanity.com/paper/2401.10298/) (2024)
- [Improving Demand Forecasting in Open Systems with Cartogram-Enhanced Deep Learning](https://mlsanity.com/paper/2403.16049/) (2024)

## Abstract (arXiv)

Recent research has shown that criminal networks have complex organizational structures, but whether this can be used to predict static and dynamic properties of criminal networks remains little explored. Here, by combining graph representation learning and machine learning methods, we show that structural properties of political corruption, police intelligence, and money laundering networks can be used to recover missing criminal partnerships, distinguish among different types of criminal and legal associations, as well as predict the total amount of money exchanged among criminal agents, all with outstanding accuracy. We also show that our approach can anticipate future criminal associations during the dynamic growth of corruption networks with significant accuracy. Thus, similar to evidence found at crime scenes, we conclude that structural patterns of criminal networks carry crucial information about illegal activities, which allows machine learning methods to predict missing information and even anticipate future criminal behavior.