Fraudulent User Detection with Time-enhanced Graph Neural Networks on E-Commerce Platforms

Yen Wen Lu; Cheng Te Li

doi:10.1109/ICCE-Taiwan58799.2023.10226654

Fraudulent User Detection with Time-enhanced Graph Neural Networks on E-Commerce Platforms

Yen Wen Lu, Cheng Te Li

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Citation (Scopus)

Abstract

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.

Original language	English
Title of host publication	2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	49-50
Number of pages	2
ISBN (Electronic)	9798350324174
DOIs	https://doi.org/10.1109/ICCE-Taiwan58799.2023.10226654
Publication status	Published - 2023
Event	2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023 - Pingtung, Taiwan Duration: 2023 Jul 17 → 2023 Jul 19

Publication series

Name	2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023 - Proceedings

Conference

Conference	2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023
Country/Territory	Taiwan
City	Pingtung
Period	23-07-17 → 23-07-19

All Science Journal Classification (ASJC) codes

Artificial Intelligence
Human-Computer Interaction
Information Systems
Information Systems and Management
Electrical and Electronic Engineering
Media Technology
Instrumentation

Access to Document

10.1109/ICCE-Taiwan58799.2023.10226654

Cite this

Lu, Y. W., & Li, C. T. (2023). Fraudulent User Detection with Time-enhanced Graph Neural Networks on E-Commerce Platforms. In 2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023 - Proceedings (pp. 49-50). (2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICCE-Taiwan58799.2023.10226654

Lu, Yen Wen ; Li, Cheng Te. / Fraudulent User Detection with Time-enhanced Graph Neural Networks on E-Commerce Platforms. 2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2023. pp. 49-50 (2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023 - Proceedings).

@inproceedings{292081b688e94c4a8b06e9c84060d731,

title = "Fraudulent User Detection with Time-enhanced Graph Neural Networks on E-Commerce Platforms",

abstract = "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.",

author = "Lu, {Yen Wen} and Li, {Cheng Te}",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023 ; Conference date: 17-07-2023 Through 19-07-2023",

year = "2023",

doi = "10.1109/ICCE-Taiwan58799.2023.10226654",

language = "English",

series = "2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023 - Proceedings",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "49--50",

booktitle = "2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023 - Proceedings",

address = "United States",

}

Lu, YW & Li, CT 2023, Fraudulent User Detection with Time-enhanced Graph Neural Networks on E-Commerce Platforms. in 2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023 - Proceedings. 2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023 - Proceedings, Institute of Electrical and Electronics Engineers Inc., pp. 49-50, 2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023, Pingtung, Taiwan, 23-07-17. https://doi.org/10.1109/ICCE-Taiwan58799.2023.10226654

Fraudulent User Detection with Time-enhanced Graph Neural Networks on E-Commerce Platforms. / Lu, Yen Wen; Li, Cheng Te.
2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2023. p. 49-50 (2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023 - Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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T1 - Fraudulent User Detection with Time-enhanced Graph Neural Networks on E-Commerce Platforms

AU - Lu, Yen Wen

AU - Li, Cheng Te

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.

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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 -

Lu YW, Li CT. Fraudulent User Detection with Time-enhanced Graph Neural Networks on E-Commerce Platforms. In 2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2023. p. 49-50. (2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023 - Proceedings). doi: 10.1109/ICCE-Taiwan58799.2023.10226654

Fraudulent User Detection with Time-enhanced Graph Neural Networks on E-Commerce Platforms

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