Graph Convolutional Networks for Fraud Detection of Bitcoin Transactions
tl;dr I trained 4 different types of models to classify bitcoin transactions. For each, two versions of the feature set were used: all features (local + neighborhood-aggregated) and only local features (without neighborhood information). The best model was a Random Forest trained with all features: its performance was impaired when the aggregated features were removed. The best graph-based neural network model was APPNP and its performance was better when only local features were used. APPNP performed better than an MLP with comparable complexity, indicating that the graph structure information gave it an advantage. Finally, the best GCN model required using all features and several strategies to reduce overfitting. The excellent performance of a Random Forest shows that it makes sense to consider simple models when faced with a new task. It also indicates that the individual node features in the Elliptic dataset are already informative enough to make good predictions. It would be interesting to explore how the model performs, when fewer samples and/or features are available for training. ...