A Deep Graph Anomaly Detection Library
based on DGL

Website | Doc

DGLD is an open-source library for Deep Graph Anomaly Detection based on pytorch and DGL. It provides unified interface of popular graph anomaly detection methods, including the data loader, data augmentation, model training and evaluation. Also, the widely used modules are well organized so that developers and researchers can quickly implement their own designed models.

News

• [Aug 2022] We have released an easy-to-use graphical command line tool for users to run experiments with different models, datasets and customized parameters. Users can select all the settings in the page, click 'Submit' and copy the shell scripts to the terminal.
• [July 2022] For PyG users, we recommend the PyGOD, which is another comprehensive package that also supports many graph anomaly detection methods.
• [June 2022] Recently we receive feedback that the reported results are slightly different from the original paper. This is due to the anomaly injection setting, the graph augmentation and sampling. We will provide more details on the settings.

Installation

Basic environment installation:

conda create -n dgld python=3.8.0
conda activate dgld
conda install cudatoolkit==11.3.1
pip install dgl-cu113==0.8.1 dglgo==0.0.1 -f https://data.dgl.ai/wheels/repo.html
pip install torch==1.11.0+cu113  -f https://download.pytorch.org/whl/torch_stable.html

Install from source code

Then clone the DGLD project, enter the directory and run:

git clone [email protected]:EagleLab-ZJU/DGLD.git
pip install -r requirements.txt

To check whether you have successfully installed the package and environment, you can simply run

python example.py

Install from pypi

pip install DGLD

Now you can enjoy DGLD!

Quick Start

We support an example.py showing how it works. Here, we introduce how to simply run DGLD, following 4 steps.

Dataloader

DGLD support multiple data import methods, including PyTorch Geometric, DGL and custom data. DGLD combines the process of data load and anomaly injection. Except for some basic datasets(including "Cora", "Citeseer", "Pubmed", "BlogCatalog", "Flickr", "ogbn-arxiv" and "ACM"), DGLD also accept custom data.

Anomaly Injection

In anomaly detection, DGLD inject the abnormal node in two methods, structural and contextual, by two parameters - p and k. gnd_dataset is an instance of GraphNodeAnomalyDectionDataset. g is an instance of DGL.Graph. label is an instnace of torch.Tensor, presenting the anomaly class. Following is an example showing that a few lines of codes are sufficient to load and inject.

from DGLD.utils.dataset import GraphNodeAnomalyDectionDataset

gnd_dataset = GraphNodeAnomalyDectionDataset("Cora", p = 15, k = 50)
g = gnd_dataset[0]
label = gnd_dataset.anomaly_label

Model

DGLD supports some basic methods. It's easy to construct and train model.

from DGLD.models import CoLA

model = CoLA(in_feats = g.ndata['feat'].shape[1])

Train and Evaluation

Function fit need parameters to specify number of epoch and device. For gpu, device should be a int, while a string 'cpu' for cpu.

from DGLD.utils.evaluation import split_auc

model.fit(g, num_epoch=5, device=0)
result = model.predict(g, auc_test_rounds=2)
print(split_auc(label, result))

Native Datasets

The DGLD provides native graph anomaly detection datasets that widely used by existing methods.

Dataset	nodes	edges	attributes	anomalies
BlogCatalog	5196	171743	8189	300
Flickr	7575	239738	12047	450
ACM	16484	71980	8337	600
Cora	2708	5429	1433	150
Citeseer	3327	4732	3703	150
Pubmed	19717	44338	500	600
ogbn-arxiv	169343	1166243	128	6000

Implemented Results (Sorted Results)

Method	Cora	Citeseer	Pubmed	BlogCatalog	Flickr	ACM	Arxiv
CoLA	0.8823	0.8765	0.9632	0.6488	0.5790	0.8194	0.8833
SL-GAD	0.8937	0.9003	0.9532	0.7782	0.7664	0.8146	0.7483
ANEMONE	0.8916	0.8633	0.9630	-	-	-	-
DOMINANT	0.8555	0.8236	0.8295	0.7795	0.7559	0.7067	-
ComGA	0.9677	0.8020	0.9205	0.7908	0.7346	0.7147	-
AnomalyDAE	0.9679	0.8832	0.9182	0.7666	0.7437	0.7091	-
ALARM	0.9479	0.8318	0.8296	0.7718	0.7596	0.6952	-
AAGNN	0.7371	0.7616	0.7442	0.7648	0.7388	0.4868	-
GUIDE	0.9785	0.9778	0.9535	0.7675	0.7337	0.7153	-
CONAD	0.9646	0.9116	0.9396	0.7863	0.7395	0.7005	0.6365
GAAN	0.7964	0.7979	0.7862	0.7320	0.7510	-	0.8605
DONE	0.9636	0.8948	0.8803	0.7842	0.7555	0.7094	0.7093
ONE	0.9717	0.9900	0.8991	0.7924	0.7712	0.7072	-
AdONE	0.9629	0.8935	0.9030	0.7438	0.7595	-	0.7651
GCNAE	0.7707	0.7696	0.7941	0.7363	0.7529	-	0.7530
MLPAE	0.7617	0.7538	0.7211	0.7399	0.7514	-	0.7382
SCAN	0.6508	0.6671	0.7361	0.4926	0.6498	-	0.6905

Upcoming Features

• More Graph Anomaly Detection Methods
• Edge/Community/Graph Level Anomaly Detection Tasks
• Graphical Operation Interface