Clustering-Augmented Fraud Detection on Graphs Using Label-Aware Feature Aggregation
Keywords: Fraud Detection, Graph Neural Networks, Heterophily, Imbalance, Clustering
Verify Author List: I have double-checked the author list and understand that additions and removals will not be allowed after the submission deadline.
Abstract: Fraud detection has emerged as a pivotal process in different fields (e.g., e-commerce, social networks). Since interactions among entities provide valuable insights into fraudulent activities, such behaviors can be naturally represented as graphs, where graph neural networks (GNNs) have been developed as prominent models to boost the efficacy of fraud detection. However, the application of GNNs in this domain encounters significant challenges, primarily due to class imbalance and a mixture of homophily and heterophily of fraud graphs. To address these challenges, in this paper, we propose LACA, which implements fraud detection on graphs using Label-Aware feature aggregation to advance GNN training, which is regularized by Clustering Augmented optimization. Specifically, label-aware feature aggregation simplifies adaptive aggregation in homophily-heterophily mixed neighborhoods, preventing gradient domination by legitimate nodes and mitigating class imbalance in message passing. Clustering-augmented optimization provides fine-grained subclass semantics to improve detection performance, and yields additional benefit in addressing class imbalance. Extensive experiments on four fraud datasets demonstrate that LACA can significantly improve fraud detection performance on graphs with different imbalance ratios and homophily ratios, outperforming state-of-the-art GNN models.
A Signed Permission To Publish Form In Pdf: pdf
Primary Area: Applications (bioinformatics, biomedical informatics, climate science, collaborative filtering, computer vision, healthcare, human activity recognition, information retrieval, natural language processing, social networks, etc.)
Paper Checklist Guidelines: I certify that all co-authors of this work have read and commit to adhering to the guidelines in Call for Papers.
Student Author: Yes
Submission Number: 353
Loading