# The Shape of Money Laundering: Subgraph Representation Learning on the Blockchain with the Elliptic2 Dataset

arXiv: https://arxiv.org/abs/2404.19109 · HTML: https://mlsanity.com/paper/2404.19109/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 29 references that are in the map.
- First posted on arXiv 29 Apr 2024.

## Builds on (most cited first, 8 of 8)

- [Open Graph Benchmark: Datasets for Machine Learning on Graphs](https://mlsanity.com/paper/2005.00687/) (2020)
- [Graph Neural Networks: A Review of Methods and Applications](https://mlsanity.com/paper/1812.08434/) (2018)
- [OGB-LSC: A Large-Scale Challenge for Machine Learning on Graphs](https://mlsanity.com/paper/2103.09430/) (2021)
- [Anti-Money Laundering in Bitcoin: Experimenting with Graph Convolutional Networks for Financial Forensics](https://mlsanity.com/paper/1908.02591/) (2019)
- [Subgraph Neural Networks](https://mlsanity.com/paper/2006.10538/) (2020)
- [Accelerating Training and Inference of Graph Neural Networks with Fast Sampling and Pipelining](https://mlsanity.com/paper/2110.08450/) (2021)
- [Communication-Efficient Graph Neural Networks with Probabilistic Neighborhood Expansion Analysis and Caching](https://mlsanity.com/paper/2305.03152/) (2023)
- [The Unreasonable Effectiveness of Address Clustering](https://mlsanity.com/paper/1605.06369/) (2016)

## Built on by (most cited first, 8 of 8)

- [Bitcoin Research with a Transaction Graph Dataset](https://mlsanity.com/paper/2411.10325/) (2024)
- [Normalisation and Initialisation Strategies for Graph Neural Networks in Blockchain Anomaly Detection](https://mlsanity.com/paper/2602.23599/) (2026)
- [Interpreting Graph Inference with Skyline Explanations](https://mlsanity.com/paper/2505.07635/) (2025)
- [A Depth-Aware Comparative Study of Euclidean and Hyperbolic Graph Neural Networks on Bitcoin Transaction Systems](https://mlsanity.com/paper/2603.16080/) (2026)
- [Refining Heuristic-Based Bitcoin Address Clustering with Graph Neural Networks](https://mlsanity.com/paper/2609.01942/) (2026)
- [Population-Calibrated Graph Screening at 835-Million-Address Scale, with Label-Free Transfer to New Chains](https://mlsanity.com/paper/2609.03036/) (2026)
- [ETH-TraceBench: A Large-Scale Event-Stream Benchmark for Ethereum DeFi under Temporal, Protocol, and Contract Shift](https://mlsanity.com/paper/2609.23659/) (2026)
- [From Imbalance to Evidence: Deep Nonparametric Testing for Complex Data under Severe Sample Imbalance](https://mlsanity.com/paper/2610.12946/) (2026)

## Abstract (arXiv)

Subgraph representation learning is a technique for analyzing local structures (or shapes) within complex networks. Enabled by recent developments in scalable Graph Neural Networks (GNNs), this approach encodes relational information at a subgroup level (multiple connected nodes) rather than at a node level of abstraction. We posit that certain domain applications, such as anti-money laundering (AML), are inherently subgraph problems and mainstream graph techniques have been operating at a suboptimal level of abstraction. This is due in part to the scarcity of annotated datasets of real-world size and complexity, as well as the lack of software tools for managing subgraph GNN workflows at scale. To enable work in fundamental algorithms as well as domain applications in AML and beyond, we introduce Elliptic2, a large graph dataset containing 122K labeled subgraphs of Bitcoin clusters within a background graph consisting of 49M node clusters and 196M edge transactions. The dataset provides subgraphs known to be linked to illicit activity for learning the set of "shapes" that money laundering exhibits in cryptocurrency and accurately classifying new criminal activity. Along with the dataset we share our graph techniques, software tooling, promising early experimental results, and new domain insights already gleaned from this approach. Taken together, we find immediate practical value in this approach and the potential for a new standard in anti-money laundering and forensic analytics in cryptocurrencies and other financial networks.