Efficient Densest Flow Queries in Transaction Flow Networks (Complete Version)

2026-02-17

AI Summary

This work proposes the (S,T)-densest monetary flow (SDMF) query problem to effectively detect illicit activities such as money laundering and credit card fraud in transaction flow networks. Formally defined for the first time, the SDMF query seeks a subgraph that maximizes flow density while satisfying cardinality constraints over a given set of source nodes S and sink nodes T. Addressing this NP-hard problem, the authors design CONAN, a divide-and-conquer algorithm enhanced with approximate flow peeling and graph density optimization techniques to improve efficiency. Extensive experiments on large-scale real-world transaction networks—including Grab and NFT datasets—demonstrate that CONAN achieves up to three orders of magnitude speedup over baseline methods, substantially enhancing both query practicality and fraud detection capability.

Technology Category

• Data Mining & Knowledge Management: Anomaly/Outlier Detection
• Search and Optimization: Distributed Search
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Web Mining and Content Analysis: Content-based information diffusion
• Security and Privacy: Large-scale security measurements

Abstract

Transaction flow networks are crucial in detecting illicit activities such as wash trading, credit card fraud, cashback arbitrage fraud, and money laundering. \revise{Our collaborator, Grab, a leader in digital payments in Southeast Asia, faces increasingly sophisticated fraud patterns in its transaction flow networks. In industry settings such as Grab's fraud detection pipeline, identifying fraudulent activities heavily relies on detecting dense flows within transaction networks. Motivated by this practical foundation,} we propose the \emph{\(S\)-\(T\) densest flow} (\SDMF{}) query. Given a transaction flow network \( G \), a source set \( \Src \), a sink set \( \Dst \), and a size threshold \( k \), the query outputs subsets \( \Src' \subseteq \Src \) and \( \Dst' \subseteq \Dst \) such that the maximum flow from \( \Src' \) to \( \Dst' \) is densest, with \(|\Src' \cup \Dst'| \geq k\). Recognizing the NP-hardness of the \SDMF{} query, we develop an efficient divide-and-conquer algorithm, CONAN. \revise{Driven by industry needs for scalable and efficient solutions}, we introduce an approximate flow-peeling algorithm to optimize the performance of CONAN, enhancing its efficiency in processing large transaction networks. \revise{Our approach has been integrated into Grab's fraud detection scenario, resulting in significant improvements in identifying fraudulent activities.} Experiments show that CONAN outperforms baseline methods by up to three orders of magnitude in runtime and more effectively identifies the densest flows. We showcase CONAN's applications in fraud detection on transaction flow networks from our industry partner, Grab, and on non-fungible tokens (NFTs).

Problem

• densest flow
• transaction flow networks
• fraud detection
• NP-hard query
• maximum flow

Innovation

• densest flow query
• transaction flow networks
• divide-and-conquer algorithm
• flow peeling
• fraud detection