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.
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
Then clone the DGLD project, enter the directory and run:
git clone git@github.com:EagleLab-ZJU/DGLD.git pip install -r requirements.txt
To check whether you have successfully installed the package and environment, you can simply run
Now you can enjoy DGLD!
We support an example.py showing how it works. Here, we introduce how to simply run DGLD, following 4 steps.
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.
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
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])
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))
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 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 - 0.6905RetroSearch is an open source project built by @garambo | Open a GitHub Issue
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