DGLD

Benchmark for Deep Graph Anomaly Detection

DGLD
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.

Build your own model with
DGLD library

DGLD provides the following properities for efficiently implement new models and fair comparison.

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Flexible data loaders

DGLD provides fully automates dataset processing that are fully compatible with DGL and Pytorch Geometric. The most advanced Graph Augmentation are built-in which supports custom model design.

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Unified building blocks

DGLD provides unified building blocks for deep graph anomaly detection, including graph neural network layers, training objectives and anomaly score estimators. The users can easily build a new model based on the blocks.

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Fair comparison environment

DGLD provides fair comparison environment for different models, including data processing, data augmentation and evaluation metric. The leaderboards list the most recent methods on benchmark datasets.

FEATURES

Flexible Usage

The DGLD users can design, train and evaluate a graph anomaly detection model by running a simple command. The running are automatelly conducted and the results can be easily obtained in the log file.