Multiplex graph fusion network with reinforcement structure learning for fraud detection in online e-commerce platforms

doi:10.1016/j.eswa.2024.125598

	Multiplex graph fusion network with reinforcement structure learning for fraud detection in online e-commerce platforms
	Zhang, Zheng 1,2; Ao, Xiang 3; Tessone, Claudio J.4,5; Liu, Gang 1; Zhou, Mingyang 1; Mao, Rui 1; Liao, Hao 1
	2025-03-01
发表期刊	EXPERT SYSTEMS WITH APPLICATIONS
ISSN	0957-4174
卷号	262 页码:12
摘要	Fraudulent activities on e-commerce platforms, such as spamming product reviews or fake payment behaviors, seriously mislead users' purchasing decisions and harm platform integrity. To effectively identify fraudsters, recent research mainly attempts to employ graph neural networks (GNNs) with aggregating neighborhood features for detecting the fraud suspiciousness. However, GNNs are vulnerable to carefully-crafted perturbations in the graph structure, and the camouflage strategies of collusive fraudsters limit the effectiveness of GNNsbased fraud detectors. To address these issues, a novel multiplex graph fusion network with reinforcement structure learning (RestMGFN) is proposed in this paper to reveal the collaborative camouflage review fraud. Specifically, an adaptive graph structure learning module is designed to generate high-quality graph representation by utilizing paradigm constraints on the intrinsic properties of graph. Multiple relation-specific graphs are then constructed using meta-path search for capturing the deep semantic features of fraudulent activities. Finally, we incorporate the multiplex graph representations module into a unified framework, jointly optimizing the graph structure and corresponding embedding representations. Comprehensive experiments on real-world datasets verify the effectiveness and robustness of the proposed model compared with state-of-the-art approaches.
关键词	Fraud detection Graph neural networks Graph structure learning Metapath Multigraph fusion
DOI	10.1016/j.eswa.2024.125598
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[62276171] ; National Natural Science Foundation of China[62476173] ; National Natural Science Foundation of China[62002233] ; Guangdong Basic and Applied Basic Research Foundation, China[2024A1515011938] ; Guangdong Basic and Applied Basic Research Foundation, China[2020B1515120028] ; Shenzhen Fundamental Research-General Project, China[20220811155803001] ; Henan Province International Science and Technology Cooperation Project, China[232102520004] ; CCF-Baidu Open Fund
WOS研究方向	Computer Science ; Engineering ; Operations Research & Management Science
WOS类目	Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic ; Operations Research & Management Science
WOS记录号	WOS:001354053200001
出版者	PERGAMON-ELSEVIER SCIENCE LTD
引用统计
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/39482
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Liao, Hao
作者单位	1.Shenzhen Univ, Coll Comp Sci & Software Engn, Shenzhen 518060, Peoples R China 2.Nanyang Inst Technol, Sch Comp & Software, Nanyang 473004, Peoples R China 3.Chinese Acad Sci, Inst Comp Technol, Beijing 100086, Peoples R China 4.Univ Zurich, UZH Blockchain Ctr, CH-8050 Zurich, Switzerland 5.Univ Zurich, URPP Social Networks, CH-8050 Zurich, Switzerland
推荐引用方式 GB/T 7714	Zhang, Zheng,Ao, Xiang,Tessone, Claudio J.,et al. Multiplex graph fusion network with reinforcement structure learning for fraud detection in online e-commerce platforms[J]. EXPERT SYSTEMS WITH APPLICATIONS,2025,262:12.
APA	Zhang, Zheng.,Ao, Xiang.,Tessone, Claudio J..,Liu, Gang.,Zhou, Mingyang.,...&Liao, Hao.(2025).Multiplex graph fusion network with reinforcement structure learning for fraud detection in online e-commerce platforms.EXPERT SYSTEMS WITH APPLICATIONS,262,12.
MLA	Zhang, Zheng,et al."Multiplex graph fusion network with reinforcement structure learning for fraud detection in online e-commerce platforms".EXPERT SYSTEMS WITH APPLICATIONS 262(2025):12.