Transaction analysis of crypto economic systems using Graph Neural Networks, particularly for classifying fraudulent activity amongst a sea of innocuous transactions.
- DGL: https://www.dgl.ai
- Pytorch Geometric: https://pytorch-geometric.readthedocs.io/en/latest/
- NetworkX
- MatplotLib
- Numpy
- Pandas
- Python standard library (csv, dateutil etc.)
- Labelled Ethereum Addresses: for a comprehensive set of 20,000 labelled Ethereum addresses labelled by illicit/licit, by address type (Wallet vs Smart Contract) and for entities (Exchanges, miners, tokens etc.) visit https://www.kaggle.com/hamishhall/labelled-ethereum-addresses.
- Bitcoin Transactions: Get the elliptic dataset from here: https://www.kaggle.com/ellipticco/elliptic-data-set/. This is a feature rich dataset of fraudulent, verified and unfledged Bitcoin transactions.
- Ethereum Transactions: Run eth/tx_by_block.ipynb to gather and save to csv all transactions between a start and end date Or run eth/tx_by_neighborhood.ipynb to gather a focused set of transactions around a central set of nodes.
- EOS Transactions: The EOS transactions and labels come from a private dataset of ERC20 token transfers, available on request.
This toolkit demonstrates how to ingest transaction lists, extract nodes and build corresponding graphs. These can then be fed into DGL or Pytorch Geometric GNNs to train for downstream classification tasks. These trained models can then be analysed to observe the nature of their predictions using visualisation tools and GNNExplainer. The latter returns edge and feature masks for the prediction of a given node, highlighting what influenced the model as such. These can be visualised to gain insight into the subtle patterns (in features and connections) of the transaction graphs.