Spade: A Real-Time Fraud Detection Framework on Evolving Graphs (Complete Version)

TL;DR

Spade is an incremental, real-time fraud detection framework that efficiently identifies dense, fraudulent communities in evolving large-scale graphs, significantly outperforming static approaches.

Contribution

Introducing Spade, a novel framework that enables real-time detection of fraud communities on dynamic graphs through incremental dense subgraph maintenance.

Findings

Detects fraudulent communities in hundreds of microseconds on million-scale graphs

Incremental peeling algorithms are up to a million times faster than static versions

Supports batch updates and customizable detection semantics

Abstract

Real-time fraud detection is a challenge for most financial and electronic commercial platforms. To identify fraudulent communities, Grab, one of the largest technology companies in Southeast Asia, forms a graph from a set of transactions and detects dense subgraphs arising from abnormally large numbers of connections among fraudsters. Existing dense subgraph detection approaches focus on static graphs without considering the fact that transaction graphs are highly dynamic. Moreover, detecting dense subgraphs from scratch with graph updates is time consuming and cannot meet the real-time requirement in industry. To address this problem, we introduce an incremental real-time fraud detection framework called Spade. Spade can detect fraudulent communities in hundreds of microseconds on million-scale graphs by incrementally maintaining dense subgraphs. Furthermore, Spade supports batch updates and edge grouping to reduce response latency. Lastly, Spade provides simple but expressive APIs for the design of evolving fraud detection semantics. Developers plug their customized suspiciousness functions into Spade which incrementalizes their semantics without recasting their algorithms. Extensive experiments show that Spade detects fraudulent communities in real time on million-scale graphs. Peeling algorithms incrementalized by Spade are up to a million times faster than the static version.