Dupin: A Parallel Framework for Densest Subgraph Discovery in Fraud Detection on Massive Graphs

Detecting fraudulent activities in financial and e-commerce transaction networks is crucial. One effective method for this is Densest Subgraph Discovery (DSD). However, deploying DSD methods in production systems faces substantial scalability challenges due to the predominantly sequential nature of existing methods, which impedes their ability to handle large-scale transaction networks and results in significant detection delays. To address these challenges, we introduce Dupin, a novel parallel processing framework designed for efficient DSD processing in billion-scale graphs. Dupin is powered by a processing engine that exploits the unique properties of the peeling process, with theoretical guarantees on detection quality and efficiency. Dupin provides user-friendly APIs for flexible customization of DSD objectives and ensures robust adaptability to diverse fraud detection scenarios. Empirical evaluations indicate that Dupin consistently outperforms several existing DSD methods, achieving performance improvements of up to two orders of magnitude compared to traditional approaches. On billion-scale graphs, Dupin demonstrates the potential to enhance the prevention of fraudulent transactions by approximately 49.5 basis points and reduces density error from 30.3% to below 5.0%, as supported by our experimental results.