中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
首页 机构 成果 学者
  
  1. CAS IR Grid
  2. 计算技术研究所
  3. 中国科学院计算技术研究所
  4. 中国科学院计算技术研究所期刊论文
  5. 英文
Multiplex graph fusion network with reinforcement structure learning for fraud detection in online e-commerce platforms

文献类型：期刊论文

作者Zhang, Zheng1,2; Ao, Xiang3; Tessone, Claudio J.4,5; Liu, Gang1; Zhou, Mingyang1; Mao, Rui1; Liao, Hao1
刊名EXPERT SYSTEMS WITH APPLICATIONS
出版日期2025-03-01
卷号262页码:12
关键词Fraud detection Graph neural networks Graph structure learning Metapath Multigraph fusion
ISSN号0957-4174
DOI10.1016/j.eswa.2024.125598
英文摘要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.
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
语种英语
WOS记录号WOS:001354053200001
出版者PERGAMON-ELSEVIER SCIENCE LTD
源URL[http://119.78.100.204/handle/2XEOYT63/39481]  
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.

入库方式： OAI收割

来源：计算技术研究所

浏览0
下载0
收藏0
其他版本

除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。