This project implements the paper "Graph Anomaly Detection Based on Hybrid Node Representation Learning" published in Neural Networks (https://doi.org/10.1016/j.neunet.2025.107169).

This project is tested on cuda 11.6 with several dependencies listed below:

pytorch=1.11.0
torch-geometric=2.0.4

Public datasets Elliptic, Yelp and Weibo used for graph anomaly detection are available for evaluation. Elliptic was first proposed in this paper, of which goal is to detect money-laundering users in bitcoin network.

python train.py --dataset weibo/yelp/elliptic

Tuned hyper-parameters could be found in config.py

You could organize your dataset into a torch_geometric.data.Data then add profile of your own dataset on config.py