MD-GCN and GCN

Introduction

This repo proposes a graph convolution Layer MD-GC-Layer with feature aggregation of multi-distance nodes through graph data modeling of bitcoin transactions, which takes into account the connection of different transaction nodes in Elliptic Data Set: This graph convolution layer is different from the traditional graph convolution layer, which can aggregate the features of neighboring nodes with different distances from a node and make better use of the connections between different bitcoin transactions. Furthermore, a multi-distance node feature aggregation graph convolutional neural network MD-GCN is designed to meet the abnormal transaction detection of bitcoin system.This repo provides MD-GCN and GCN code.

Requirements

• Python 3.7.4
• Pytorch 1.2.0
• CUDA 9.2
• Pandas 0.25.3

Elliptc Data Set Preparation

Download from https://www.kaggle.com/datasets/ellipticco/elliptic-data-set
Data loading and preprocessing is in datalodader.py

MD-GCN

MD-GC-Layer

This repo proposes the MD-GC-Layer to aggregate the multi-distance nodes transactions features to make fully use of the connection of different transactions. The module is listed as follow:

And the formula is as follow:

Multi-Distance Feature Aggregation Graph Convolution Neural Network(MD-GCN)

Training

Train the GCN model

python train.py --skip False --higherorder False --lr 0.001 --node_embedding 100 --data_root your_data_root_path --end_ts 49 --max_train_ts 34 --seed 8

• --skip is whether to use skip-gcn or not.
• --node_embedding is out channels of the the first GC-Layer.
• --lr is the learning rate.
• --data_root is the Elliptic Data Set root path.
• --max_train_ts is the end time step of train set.

Train the MD-GCN model

python train.py --skip False --higherorder True --lr 0.001 --order 2 --node_embedding 100 --out_features 90 --data_root your_data_root_path --end_ts 49 --max_train_ts 34 --seed 8

• --skip is whether to use skip-gcn or not.
• --higherorder is whether to use multi-distance modules or not.
• --order is the number of the distance.
• --node_embedding is the output channels of the first GC-MD-Layer.
• --out_features is the output channels of the second GC-MD-Layer.

Testing

Test the model and outputs Precision, Recall and F1 Score.

python test.py

This repo also choose to draw the Confusion Matrix, Precison-Recall Curve, ROC Curve.

python visualize.py

Results on Elliptic Data Set

Several models includes MD-GCN and GCN are tested on Elliptic Data Set, and the results are listed as follow: