Pytorch Implementation of
Alleviating Structrual Distribution Shift in Graph Anomaly Detection (WSDM 2023)
This work solves the SDS problem from a feature view. We observe that the heterophily degree is different across the training and test environments in GAD, leading to the poor generalization of the classifier. To address the issue, we propose GDN to resist high heterophily for anomalies meanwhile benefit the learning of normals from homophily. Since different labels correspond to the difference of critical anomaly features which make great contributions to the GAD, we tease out the anomaly features on which we constrain to mitigate the effect of heterophilous neighbors and make them invariant. To better estimate the prior distribution of anomaly features, we devise a prototype vector to infer and update this distribution during training. For normal nodes, we constrain the remaining features to preserve the connectivity of nodes and reinforce the influence of the homophilous neighborhood.
Illustration of GDN. The feature separation module separates the node feature into two sets. Two constraints are leveraged to assist separation. Blank positions in node representation mean they are zero when calculating losses.
YelpChi and Amazon can be downloaded from here or dgl.data.FraudDataset.
Run python src/data_process.py to pre-process the data.
Please set up the environment following Requirements in this repository.
argparse 1.1.0
networkx 1.11
numpy 1.16.4
scikit_learn 0.21rc2
scipy 1.2.1
torch 1.4.0python main.py --config ./config/gdn_yelpchi.ymlOur code references:
@inproceedings{
gao2023gdn,
title={Alleviating Structrual Distribution Shift in Graph Anomaly Detection},
author={Yuan Gao and Xiang Wang and Xiangnan He and Zhenguang Liu and Huamin Feng and Yongdong Zhang},
booktitle={WSDM},
year={2023},
}