Summary of The Shape Of Money Laundering: Subgraph Representation Learning on the Blockchain with the Elliptic2 Dataset, by Claudio Bellei et al.
The Shape of Money Laundering: Subgraph Representation Learning on the Blockchain with the Elliptic2 Dataset
by Claudio Bellei, Muhua Xu, Ross Phillips, Tom Robinson, Mark Weber, Tim Kaler, Charles E. Leiserson, Arvind, Jie Chen
First submitted to arxiv on: 29 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: General Finance (q-fin.GN)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper introduces subgraph representation learning, a technique that analyzes local structures within complex networks using Graph Neural Networks (GNNs). The authors argue that certain applications, such as anti-money laundering (AML), are inherently subgraph problems and traditional graph techniques have been operating at a suboptimal level of abstraction. They propose Elliptic2, a large graph dataset containing 122K labeled subgraphs of Bitcoin clusters, which provides subgraphs linked to illicit activity for learning the set of “shapes” that money laundering exhibits in cryptocurrency. The authors share their graph techniques, software tooling, early experimental results, and new domain insights, highlighting the potential for this approach in anti-money laundering and forensic analytics. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a way to learn patterns in big networks, like those used in cryptocurrencies. It’s called subgraph representation learning and uses special computer programs called Graph Neural Networks (GNNs). The authors think that some problems, like stopping money laundering, are best solved by looking at small groups of nodes within these big networks rather than the whole network. They created a big dataset with 122K examples of these small groups, or subgraphs, and showed how this approach can be useful for fighting financial crimes. |
Keywords
» Artificial intelligence » Representation learning
