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Unsupervised Graph Outlier Detection: Problem Revisit, New Insight, and Superior Method

This is the source code of paper ”Unsupervised Graph Outlier Detection: Problem Revisit, New Insight, and Superior Method“ (https://arxiv.org/abs/2210.12941)

This paper has been accepted by ICDE 2023.

The previous title of paper is "Are we really making much progress in unsupervised graph outlier detection? Revisiting the problem with new insight and method".

VGOD-framework

Requirements

This code requires the following:

Python>=3.7
PyTorch>=1.12.1
Numpy>=1.19.2
Scipy>=1.6.2
Scikit-learn>=0.24.1
PyG >= 2.1.0 && PyG <= 2.4.0

Running the experiments

UNOD experiment

step 1: inject outlier

This is a pre-processing step which injects outliers into the original clean datasets. Take Cora dataset as an example:

python inject_anomaly.py --dataset cora

After outlier injection, the disturbed datasets are saved into "data" folder

step 2: outlier detection

This step is to run the VGOD framework to detect outliers in the network datasets. Take Cora dataset as an example:

python train_sep.py --data cora

Structural outlier detection experiment under different injection parameters

step 1: inject outliers

This is a pre-processing step which injects outliers into the original clean datasets.

python inject_struct_anomaly_groups.py

After outlier injection, the disturbed datasets are saved into "struct_datasets" folder

step 2: outlier detection

This step is to run the VBM to detect outliers in the network datasets.

python struct_ano_detect_groups.py --data Cora

python struct_ano_detect_groups.py --data Citeseer

python struct_ano_detect_groups.py --data PubMed

python struct_ano_detect_groups.py --data Flickr

Structural outlier detection experiment under a new injection approach

Since the current injection approach will cause a serious data leakage issue as mentioned in our paper, we devise a new structural outlier injection approach to avoid the data leakage issue. More details can be found in our paper.

step 1: inject outliers

This is a pre-processing step which injects outliers into the original clean datasets.

python inject_strcut_anomaly_new_approach.py

After outlier injection, the disturbed datasets are saved into "NovelStr_datasets" folder

step 2: outlier detection

This step is to run the VBM to detect outliers in the network datasets.

python struct_ano_detect_new_approach.py