Crowdsourcing Fraud Detection over Heterogeneous Temporal MMMA Graph

TL;DR

This paper introduces CMT, a contrastive multi-view learning method that effectively detects crowdsourcing frauds in heterogeneous temporal graphs of messaging apps, outperforming existing approaches and demonstrating broad applicability.

Contribution

The paper presents a novel self-supervised learning approach, CMT, that captures heterogeneity and dynamics in temporal graphs for fraud detection, advancing the state-of-the-art.

Findings

CMT significantly outperforms existing methods on industry-size HTG of WeChat.

CMT achieves promising results on large-scale public financial HTG.

The approach is applicable to various graph anomaly detection tasks.

Abstract

The rise of the click farm business using Multi-purpose Messaging Mobile Apps (MMMAs) tempts cybercriminals to perpetrate crowdsourcing frauds that cause financial losses to click farm workers. In this paper, we propose a novel contrastive multi-view learning method named CMT for crowdsourcing fraud detection over the heterogeneous temporal graph (HTG) of MMMA. CMT captures both heterogeneity and dynamics of HTG and generates high-quality representations for crowdsourcing fraud detection in a self-supervised manner. We deploy CMT to detect crowdsourcing frauds on an industry-size HTG of a representative MMMA WeChat and it significantly outperforms other methods. CMT also shows promising results for fraud detection on a large-scale public financial HTG, indicating that it can be applied in other graph anomaly detection tasks. We provide our implementation at https://github.com/KDEGroup/CMT.