An Unsupervised Graph-based Toolbox for Fraud Detection
UGFraud
UGFraud is an unsupervised graph-based fraud detection toolbox that integrates several state-of-the-art graph-based fraud detection algorithms. It can be applied to bipartite graphs (e.g., user-product graph), and it can estimate the suspiciousness of both nodes and edges. The implemented models can be found here.
The toolbox incorporates the Markov Random Field (MRF)-based algorithm, dense-block detection-based algorithm, and SVD-based algorithm. For MRF-based algorithms, the users only need the graph structure and the prior suspicious score of the nodes as the input. For other algorithms, the graph structure is the only input.
Meanwhile, we have a deep graph-based fraud detection toolbox which implements state-of-the-art graph neural network-based fraud detectors.
We welcome contributions on adding new fraud detectors and extending the features of the toolbox. Some of the planned features are