Zheng Zhang , Xiang Ao , Claudio J. Tessone , Gang Liu , Mingyang Zhou , Rui Mao , Hao Liao
{"title":"Multiplex graph fusion network with reinforcement structure learning for fraud detection in online e-commerce platforms","authors":"Zheng Zhang , Xiang Ao , Claudio J. Tessone , Gang Liu , Mingyang Zhou , Rui Mao , Hao Liao","doi":"10.1016/j.eswa.2024.125598","DOIUrl":null,"url":null,"abstract":"<div><div>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 GNNs-based 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.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":null,"pages":null},"PeriodicalIF":7.5000,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Expert Systems with Applications","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0957417424024655","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0
Abstract
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 GNNs-based 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.
期刊介绍:
Expert Systems With Applications is an international journal dedicated to the exchange of information on expert and intelligent systems used globally in industry, government, and universities. The journal emphasizes original papers covering the design, development, testing, implementation, and management of these systems, offering practical guidelines. It spans various sectors such as finance, engineering, marketing, law, project management, information management, medicine, and more. The journal also welcomes papers on multi-agent systems, knowledge management, neural networks, knowledge discovery, data mining, and other related areas, excluding applications to military/defense systems.