Towards Graph-level anomaly detection via deep evolutionary mapping

Open-sourced implementation for GmapAD - KDD 2023.

GmapAD is a graph-level anomaly detection framework with specially designed explainable graph mapping that maps graphs into a latent space where anomalies can be effectively detected. GmapAD's framework is shown as above.

Python Dependencies

Our proposed GmapAD framework is implemented in Python 3.7 and major libraries include:

Pytorch = 1.11.0+cu102
[PyG] (https://pytorch-geometric.readthedocs.io/en/latest/) torch-geometric=2.1.0

More dependencies are provided in requirements.txt.

To Run

python src/main.py

Datasets

All datasets used in this paper are from previous works and the brain network datasets can be downloaded from BrainNetDatasets and graph classification datasets can be downloaded from GraphClsDatasets.