Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

6-2025

Abstract

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.

Keywords

Graph Algorithms, Parallel Programming, Densest Subgraph Discovery

Discipline

Graphics and Human Computer Interfaces | Programming Languages and Compilers | Theory and Algorithms

Research Areas

Intelligent Systems and Optimization

Areas of Excellence

Digital transformation

Publication

Proceedings of the ACM on Management of Data, Volume 3, Issue 3, Berlin, Germany, June 22-27

First Page

1

Last Page

26

Identifier

10.1145/3725287

Publisher

ACM

City or Country

New York

Additional URL

https://doi.org/10.1145/3725287

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