Group-based Fraud Detection Network on e-Commerce Platforms

Yu, Jianke; Wang, Hanchen; Wang, Xiaoyang; Li, Zhao; Qin, Lu; Zhang, Wenjie; Liao, Jian; Zhang, Ying

doi:10.1145/3580305.3599836

Cited by 10 publications

(1 citation statement)

References 57 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These approaches have been used in various applications such as social network analysis, protein function prediction, and knowledge graph completion [43]. In addition, there are some GNN models that have been specifically optimized for bipartite graph problems [54,56,57]. However, these methods cannot utilize temporal information and are therefore not suitable for application on temporal bipartite graphs.…”

Section: Related Workmentioning

confidence: 99%

FPGN: follower prediction framework for infectious disease prevention

Yu,

Zhang,

Wang

et al. 2023

World Wide Web

Self Cite

View full text Add to dashboard Cite

In recent years, how to prevent the widespread transmission of infectious diseases in communities has been a research hot spot. Tracing close contact with infected individuals is one of the most severe problems. In this work, we present a model called Follower Prediction Graph Network (FPGN) to identify high-risk visitors, which is known as follower prediction. The model is designed to identify visitors who may be infected with a disease by tracking their activities at the exact location of infected visitors. FPGN is inspired by the state-of-the-art temporal graph edge prediction algorithm TGN and draws on the shortcomings of existing algorithms. It utilizes graph structure information based on ($$\alpha $$ α , $$\beta $$ β )-core, time interval statistics by using the statistics of timestamp information, and a GAT-based prediction module to achieve high accuracy in follower prediction. Extensive experiments are conducted on two real datasets, demonstrating the progress of FPGN. The experimental results show that FPGN can achieve the highest results compared with other SOTA baselines. Its AP scores are higher than 0.46, and its AUC scores are higher than 0.62.

show abstract