Awesome

<p> <img src="figs/Framework.png" width="1000"> <br /> </p> <hr> <h1> Towards Graph-level anomaly detection via deep evolutionary mapping </h1>

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.

<h2> Python Dependencies </h2>

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.

python src/main.py

<h2> Datasets </h2>

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.

<h2> Baselines </h2>

As provided in the Appendix of our manuscript, all baselines and their URLs are:

WWL (https://github.com/BorgwardtLab/WWL).

g-U-Nets (https://github.com/HongyangGao/Graph-U-Nets).

SAGPool (https://github.com/inyeoplee77/SAGPool).

DIFFPOOL (https://github.com/RexYing/diffpool).

GMT (https://github.com/JinheonBaek/GMT).

OCGIN (https://github.com/LingxiaoShawn/GLOD-Issues).

OCGTL (https://github.com/boschresearch/GraphLevel-AnomalyDetection).

GLocalKD (https://github.com/RongrongMa/GLocalKD).

iGAD (https://github.com/graph-level-anomalies/iGAD/tree/main).