TY - GEN
T1 - Fraudulent User Detection with Time-enhanced Graph Neural Networks on E-Commerce Platforms
AU - Lu, Yen Wen
AU - Li, Cheng Te
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - In this paper, we propose a Graph Neural Network-based model to detect fraudulent users on e-commerce platforms without relying on rating scores. Utilizing user-product bipartite graphs and timestamp data, we capture temporal patterns and neighborhood information, creating a graph with multidimensional edge vectors. Our model demonstrates competitive performance compared to state-of-the-art methods, effectively identifying fraudulent users under data-insufficient conditions and enhancing the overall reliability of online platforms.
AB - In this paper, we propose a Graph Neural Network-based model to detect fraudulent users on e-commerce platforms without relying on rating scores. Utilizing user-product bipartite graphs and timestamp data, we capture temporal patterns and neighborhood information, creating a graph with multidimensional edge vectors. Our model demonstrates competitive performance compared to state-of-the-art methods, effectively identifying fraudulent users under data-insufficient conditions and enhancing the overall reliability of online platforms.
UR - http://www.scopus.com/inward/record.url?scp=85174938761&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85174938761&partnerID=8YFLogxK
U2 - 10.1109/ICCE-Taiwan58799.2023.10226654
DO - 10.1109/ICCE-Taiwan58799.2023.10226654
M3 - Conference contribution
AN - SCOPUS:85174938761
T3 - 2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023 - Proceedings
SP - 49
EP - 50
BT - 2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023
Y2 - 17 July 2023 through 19 July 2023
ER -