Deceptive reviewer group detection using self-adversarial variational autoencoder: a heterogeneous graph-based approach

With the rapid growth of online review platforms and e-commerce websites, user-posted reviews have become an essential factor influencing consumers' purchasing decisions. The growing presence of review spammers who post misleading or biased reviews has raised concerns about the credibility of o...

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Vydáno v:	Knowledge and information systems Ročník 67; číslo 11; s. 10581 - 10610
Hlavní autoři:	Maurya, Sushil Kumar, Singh, Dinesh
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	London Springer London 01.11.2025 Springer Nature B.V
Témata:	Behavior Clustering Computer Science Data Mining and Knowledge Discovery Database Management Datasets Deep learning Graphs Information Storage and Retrieval Information Systems and Communication Service Information Systems Applications (incl.Internet) IT in Business Machine learning Nodes Product reviews Propagation Random walk Spamming Variational autoencoder Self-adversarial Heterogeneous graph Spammer group Clustering Meta-walk
ISSN:	0219-1377, 0219-3116
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Abstract	With the rapid growth of online review platforms and e-commerce websites, user-posted reviews have become an essential factor influencing consumers' purchasing decisions. The growing presence of review spammers who post misleading or biased reviews has raised concerns about the credibility of online platforms. Due to the difficulty arising from the absence of evident behavioral cues among solitary reviewers, our proposal entails inferring the concealed associations between reviewers and products by completing the user-review-product graph. To accomplish this, we propose an integrated approach comprising three key components. The first component aims to construct comprehensive reviewer node embeddings to capture the essence of user behaviors. We introduce a novel approach called the Weighted Node Random Walk Learning-Based Heterogeneous Graph (WNRWL-HG) to achieve reviewer node embeddings. The second component is designed to identify varying densities among reviewer nodes, utilizing the OPTICS (Ordering Points to Identify the Clustering Structure) clustering technique. The OPTICS technique analyzes the underlying distribution patterns within the clustering structure, offering a more nuanced perspective for identifying candidate reviewer groups. In the last component, a Self-Adversarial Variational Autoencoder (SA-VAE) model is constructed to combat the infiltration of active review spammers within the candidate groups. Experimental results on two real-world review datasets—YelpZip and AmazonBook—demonstrate that our approach outperforms state-of-the-art baselines, achieving improvements of up to 4–5% in precision among the top 100 detected groups.
AbstractList	With the rapid growth of online review platforms and e-commerce websites, user-posted reviews have become an essential factor influencing consumers' purchasing decisions. The growing presence of review spammers who post misleading or biased reviews has raised concerns about the credibility of online platforms. Due to the difficulty arising from the absence of evident behavioral cues among solitary reviewers, our proposal entails inferring the concealed associations between reviewers and products by completing the user-review-product graph. To accomplish this, we propose an integrated approach comprising three key components. The first component aims to construct comprehensive reviewer node embeddings to capture the essence of user behaviors. We introduce a novel approach called the Weighted Node Random Walk Learning-Based Heterogeneous Graph (WNRWL-HG) to achieve reviewer node embeddings. The second component is designed to identify varying densities among reviewer nodes, utilizing the OPTICS (Ordering Points to Identify the Clustering Structure) clustering technique. The OPTICS technique analyzes the underlying distribution patterns within the clustering structure, offering a more nuanced perspective for identifying candidate reviewer groups. In the last component, a Self-Adversarial Variational Autoencoder (SA-VAE) model is constructed to combat the infiltration of active review spammers within the candidate groups. Experimental results on two real-world review datasets—YelpZip and AmazonBook—demonstrate that our approach outperforms state-of-the-art baselines, achieving improvements of up to 4–5% in precision among the top 100 detected groups.
Author	Singh, Dinesh Maurya, Sushil Kumar
Author_xml	– sequence: 1 givenname: Sushil Kumar surname: Maurya fullname: Maurya, Sushil Kumar email: smaurya@mnnit.ac.in, sushilbbiet@gmail.com organization: Computer Science and Engineering, Motilal Nehru National Institute of Technology Allahabad – sequence: 2 givenname: Dinesh surname: Singh fullname: Singh, Dinesh organization: Computer Science and Engineering, Motilal Nehru National Institute of Technology Allahabad
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Cites_doi	10.1109/ICC.2019.8761650 10.1016/j.jksuci.2019.10.002 10.1016/j.ipm.2019.102140 10.1007/s11042-025-20922-y 10.1007/978-981-10-3376-6_30 10.1016/j.ins.2020.05.084 10.1007/s10489-022-03427-1 10.1007/s10618-020-00693-w 10.1007/978-3-319-23528-8_17 10.1016/j.eswa.2018.06.028 10.1016/j.eswa.2020.113465 10.1609/icwsm.v11i1.14887 10.1007/s10660-020-09413-4 10.1109/TSMC.2022.3205365 10.1007/s11042-023-17348-9 10.1007/s00500-019-04107-y 10.1186/s42400-023-00159-8 10.1145/2783258.2783370 10.1007/s10489-018-1142-1 10.1016/j.ipm.2023.103282 10.1145/2623330.2623732 10.1145/2187836.2187863 10.1109/AIC55036.2022.9848811 10.1145/2505515.2505700 10.1007/s10115-017-1068-7 10.1109/TCSS.2023.3243139 10.1016/j.ins.2020.03.063 10.1007/978-981-99-8318-6_2 10.1145/304181.304187 10.1145/2939672.2939754 10.1016/j.eswa.2022.117482 10.1016/j.knosys.2020.105520 10.1109/TKDE.2018.2880192 10.1016/j.dss.2022.113911 10.1093/comjnl/bxv068 10.1109/TBDATA.2022.3177455 10.1016/j.eswa.2022.119454 10.3390/info7010012 10.1007/s10618-014-0365-y 10.1016/j.eswa.2018.07.005 10.1016/j.ins.2022.05.086 10.1145/1871437.1871557 10.1109/ICDM.2015.62 10.1016/j.asoc.2020.107023 10.1145/3394486.3403135
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References	Z Zhang (2542_CR46) 2023; 216 2542_CR20 ZW Zhang (2542_CR48) 2022; 53 S Saumya (2542_CR18) 2022; 22 F Zhang (2542_CR38) 2022; 203 SA Shinde (2542_CR47) 2024; 83 LY Dong (2542_CR22) 2018; 114 Z Wang (2542_CR11) 2016; 59 D Zhang (2542_CR32) 2023; 166 M Ankerst (2542_CR14) 1999; 28 RM Saeed (2542_CR19) 2022; 34 S Noekhah (2542_CR29) 2020; 57 L Li (2542_CR7) 2018; 41 2542_CR10 2542_CR9 A Ligthart (2542_CR24) 2021; 101 2542_CR8 J Chao (2542_CR39) 2022; 2022 2542_CR3 Y Liu (2542_CR6) 2018; 112 N Cao (2542_CR16) 2020; 156 G Giasemidis (2542_CR23) 2018; 32 MZ Asghar (2542_CR5) 2020; 24 X Wang (2542_CR42) 2022; 9 2542_CR17 2542_CR43 2542_CR44 2542_CR45 Z Wang (2542_CR13) 2020; 34 F Zhang (2542_CR31) 2020; 193 2542_CR40 2542_CR41 Z Wang (2542_CR12) 2018; 48 L Akoglu (2542_CR1) 2015; 29 X Tang (2542_CR4) 2020; 526 Q Zhang (2542_CR30) 2023; 6 Z Wang (2542_CR28) 2018; 55 2542_CR49 2542_CR33 2542_CR34 2542_CR35 F Zhang (2542_CR26) 2022; 606 SJ Ji (2542_CR27) 2020; 536 W Zhang (2542_CR25) 2016; 7 L Wu (2542_CR2) 2017; 11 2542_CR36 F Zhang (2542_CR37) 2023; 60 SK Maurya (2542_CR15) 2023; 53 RY Lau (2542_CR21) 2012; 2
References_xml	– ident: 2542_CR44 doi: 10.1109/ICC.2019.8761650 – volume: 34 start-page: 1407 issue: 1 year: 2022 ident: 2542_CR19 publication-title: J King Saud Univ-Comput Inform Sci doi: 10.1016/j.jksuci.2019.10.002 – volume: 57 issue: 1 year: 2020 ident: 2542_CR29 publication-title: Inf Process Manag doi: 10.1016/j.ipm.2019.102140 – ident: 2542_CR49 doi: 10.1007/s11042-025-20922-y – ident: 2542_CR20 doi: 10.1007/978-981-10-3376-6_30 – volume: 41 start-page: 946 issue: 4 year: 2018 ident: 2542_CR7 publication-title: Chin J Comput – volume: 536 start-page: 454 year: 2020 ident: 2542_CR27 publication-title: Inf Sci doi: 10.1016/j.ins.2020.05.084 – volume: 53 start-page: 2189 issue: 2 year: 2023 ident: 2542_CR15 publication-title: Appl Intell doi: 10.1007/s10489-022-03427-1 – volume: 34 start-page: 1621 year: 2020 ident: 2542_CR13 publication-title: Data Min Knowl Discov doi: 10.1007/s10618-020-00693-w – ident: 2542_CR35 doi: 10.1007/978-3-319-23528-8_17 – volume: 112 start-page: 148 year: 2018 ident: 2542_CR6 publication-title: Expert Syst Appl doi: 10.1016/j.eswa.2018.06.028 – volume: 2022 start-page: 1 year: 2022 ident: 2542_CR39 publication-title: Secur Commun Netw – volume: 156 year: 2020 ident: 2542_CR16 publication-title: Expert Syst Appl doi: 10.1016/j.eswa.2020.113465 – volume: 11 start-page: 319 issue: 1 year: 2017 ident: 2542_CR2 publication-title: Proceedings of the international AAAI conference on web and social media doi: 10.1609/icwsm.v11i1.14887 – volume: 22 start-page: 113 issue: 1 year: 2022 ident: 2542_CR18 publication-title: Electron Commer Res doi: 10.1007/s10660-020-09413-4 – volume: 53 start-page: 1748 issue: 3 year: 2022 ident: 2542_CR48 publication-title: IEEE Trans Syst Man Cybernet: Syst doi: 10.1109/TSMC.2022.3205365 – volume: 83 start-page: 45111 issue: 15 year: 2024 ident: 2542_CR47 publication-title: Multim Tools Appl doi: 10.1007/s11042-023-17348-9 – volume: 24 start-page: 3475 year: 2020 ident: 2542_CR5 publication-title: Soft Comput doi: 10.1007/s00500-019-04107-y – volume: 6 start-page: 26 issue: 1 year: 2023 ident: 2542_CR30 publication-title: Cybersecurity doi: 10.1186/s42400-023-00159-8 – ident: 2542_CR10 doi: 10.1145/2783258.2783370 – volume: 48 start-page: 3094 year: 2018 ident: 2542_CR12 publication-title: Appl Intell doi: 10.1007/s10489-018-1142-1 – volume: 60 issue: 3 year: 2023 ident: 2542_CR37 publication-title: Inf Process Manag doi: 10.1016/j.ipm.2023.103282 – ident: 2542_CR40 doi: 10.1145/2623330.2623732 – ident: 2542_CR8 doi: 10.1145/2187836.2187863 – ident: 2542_CR33 doi: 10.1109/AIC55036.2022.9848811 – ident: 2542_CR36 doi: 10.1145/2505515.2505700 – ident: 2542_CR43 – volume: 55 start-page: 571 year: 2018 ident: 2542_CR28 publication-title: Knowl Inf Syst doi: 10.1007/s10115-017-1068-7 – ident: 2542_CR45 doi: 10.1109/TCSS.2023.3243139 – volume: 526 start-page: 274 year: 2020 ident: 2542_CR4 publication-title: Inf Sci doi: 10.1016/j.ins.2020.03.063 – ident: 2542_CR17 doi: 10.1007/978-981-99-8318-6_2 – volume: 28 start-page: 49 issue: no. 2 year: 1999 ident: 2542_CR14 publication-title: ACM Sigmod Rec doi: 10.1145/304181.304187 – ident: 2542_CR41 doi: 10.1145/2939672.2939754 – volume: 203 year: 2022 ident: 2542_CR38 publication-title: Expert Syst Appl doi: 10.1016/j.eswa.2022.117482 – volume: 193 year: 2020 ident: 2542_CR31 publication-title: Knowl-Based Syst doi: 10.1016/j.knosys.2020.105520 – volume: 32 start-page: 1 issue: 1 year: 2018 ident: 2542_CR23 publication-title: IEEE Trans Knowl Data Eng doi: 10.1109/TKDE.2018.2880192 – volume: 166 year: 2023 ident: 2542_CR32 publication-title: Decis Support Syst doi: 10.1016/j.dss.2022.113911 – volume: 59 start-page: 861 issue: no. 6 year: 2016 ident: 2542_CR11 publication-title: Comput J doi: 10.1093/comjnl/bxv068 – volume: 9 start-page: 415 issue: 2 year: 2022 ident: 2542_CR42 publication-title: IEEE Trans Big Data doi: 10.1109/TBDATA.2022.3177455 – volume: 216 year: 2023 ident: 2542_CR46 publication-title: Expert Syst Appl doi: 10.1016/j.eswa.2022.119454 – volume: 7 start-page: 12 issue: 1 year: 2016 ident: 2542_CR25 publication-title: Information doi: 10.3390/info7010012 – volume: 29 start-page: 626 year: 2015 ident: 2542_CR1 publication-title: Data Min Knowl Discov doi: 10.1007/s10618-014-0365-y – volume: 2 start-page: 1 issue: 4 year: 2012 ident: 2542_CR21 publication-title: ACM Trans Manag Inform Syst (TMIS) – volume: 114 start-page: 210 year: 2018 ident: 2542_CR22 publication-title: Expert Syst Appl doi: 10.1016/j.eswa.2018.07.005 – volume: 606 start-page: 819 year: 2022 ident: 2542_CR26 publication-title: Inf Sci doi: 10.1016/j.ins.2022.05.086 – ident: 2542_CR3 doi: 10.1145/1871437.1871557 – ident: 2542_CR9 doi: 10.1109/ICDM.2015.62 – volume: 101 year: 2021 ident: 2542_CR24 publication-title: Appl Soft Comput doi: 10.1016/j.asoc.2020.107023 – ident: 2542_CR34 doi: 10.1145/3394486.3403135
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Title	Deceptive reviewer group detection using self-adversarial variational autoencoder: a heterogeneous graph-based approach
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