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
Rating
1641
Battle Count: 62
Relevance
6/10
While not directly applicable to trading, the techniques could be adapted for detecting anomalous patterns in financial transaction networks
Implementation Complexity
8/10
Requires handling of large-scale graph data and implementation of complex graph neural network architectures
Reproducibility
4/5
Dataset and code are publicly available, but full reproduction may require significant computational resources
About this paper
Methodology: Subgraph Representation Learning. Problem types: Binary Classification, Subgraph Classification.
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