HimNet

Code for Graph-level Anomaly Detection via Hierarchical Memory Networks (HimNet) (ECML-PKDD 2023)

Abstract

Graph-level anomaly detection aims to identify abnormal graphs that exhibit deviant structures and node attributes compared to the majority in a graph set. One primary challenge is to learn normal patterns manifested in both fine-grained and holistic views of graphs for identifying graphs that are abnormal in part or in whole. To tackle this challenge, we propose a novel approach called Hierarchical Memory Networks (HimNet), which learns hierarchical memory modules – node and graph memory modules – via a graph autoencoder network architecture. The node-level memory module is trained to model fine-grained, internal graph interactions among nodes for detecting locally abnormal graphs, while the graph-level memory module is dedicated to the learning of holistic normal patterns for detecting globally abnormal graphs. The two modules are jointly optimized to detect both locally- and globally anomalous graphs.

Data Preparation

Some of datasets are in ./dataset folder. Due to the large file size limitation, some datasets are not uploaded in this project. You may download them from the urls listed in the paper.

Train

For datasets except HSE, p53, MMP, PPAR-gamma and hERG, run the following code:

python train.py --DS dataset --feature default/deg-num

For HSE, p53, MMP and PPAR-gamma, run the following code:

python train_tox.py --DS dataset

For hERG, run the following code:

python train_herg.py

Citation

@inproceedings{niu2023graph,
  title={Graph-Level Anomaly Detection via Hierarchical Memory Networks},
  author={Niu, Chaoxi and Pang, Guansong and Chen, Ling},
  booktitle={Joint European Conference on Machine Learning and Knowledge Discovery in Databases},
  pages={201--218},
  year={2023},
  organization={Springer}
}