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

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Deep LearningLUNAR
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

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

Owner
Adam Goodge
Adam Goodge
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