Official Implementation of "LUNAR: Unifying Local Outlier Detection Methods via Graph Neural Networks"

Last update: Dec 28, 2022

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Deep Learning LUNAR

Overview

LUNAR

Official Implementation of "LUNAR: Unifying Local Outlier Detection Methods via Graph Neural Networks"

Adam Goodge, Bryan Hooi, Ng See Kiong and Ng Wee Siong (AAAI2022)

Files

main.py
variables.py : hyperparameters
utils.py : functions for loading datasets, pre-processing, graph construction, negative-sampling
LUNAR.py : GNN model and training procedure
requirements.txt : packages for virtualenv
data.zip : files for the HRSS dataset
saved_models.zip : pretrained LUNAR models for HRSS with neighbour count k = 100 and "Mixed" negative sampling

Data

MI-F/MI-V: https://www.kaggle.com/shasun/tool-wear-detection-in-cnc-mill
OPTDIGITS/PENDIGITS/SATELLITE/SHUTTLE/THYROID: http://odds.cs.stonybrook.edu

Experiments

Firstly, extract data.zip

To replicate the results on the HRSS dataset with neighbour count k = 100 and "Mixed" negative sampling scheme

Extract saved_models.zip
Run:

python3 main.py --dataset HRSS --samples MIXED --k 100

To train a new model:

Run:

python3 main.py --dataset HRSS --samples MIXED --k 100 --train_new_model

Citation

TBC

Official Implementation of "LUNAR: Unifying Local Outlier Detection Methods via Graph Neural Networks"

Related tags

Overview

LUNAR

Official Implementation of "LUNAR: Unifying Local Outlier Detection Methods via Graph Neural Networks"

Files

Data

Experiments

Citation

Owner

Adam Goodge

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