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Detection of spammer groups helps purify the e-commerce environment and enhances consumers\u2019 shopping experience. However, existing graph-based methods for detecting spammer groups first learn user node vector representations from the graph, and then use clustering methods to obtain candidate groups. Such separate two-stage detection methods are difficult to obtain high-quality candidate groups, resulting in suboptimal detection performance. Additionally, current graph construction methods used in spammer group detection do not fully consider the characteristics of spammer groups, which limits the detection performance. Aiming these concerns, we integrate heterogeneous graph attention network (HGAN) with label propagation (LP) for detecting spammer groups. First, we build a heterogeneous weighted directed (HWD) graph by analyzing the dataset and assign an initial label to each node. Then, we integrate a HGAN-module with an LP-module to obtain the HWD graph\u2019s node embeddings and simultaneously generate candidate groups. We enhance the quality of embeddings and groups through the collaborative optimization between the predicted labels obtained from the HGAN-module and the pseudo-labels obtained from the LP-module. Finally, we calculate the suspiciousness values of groups using the reconstruction loss of the autoencoder for spammer group identification. Experiments conducted on real-world review datasets, including Amazon, Yelp, and YelpChi, demonstrate that our method achieves significant improvements in average Precision@k and Recall@k metrics compared with state-of-the-art baseline approaches.<\/jats:p>","DOI":"10.1145\/3749846","type":"journal-article","created":{"date-parts":[[2025,7,22]],"date-time":"2025-07-22T22:39:57Z","timestamp":1753223997000},"page":"1-28","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Integrating Heterogeneous Graph Attention Network with Label Propagation for Detecting Spammer Groups on E-Commerce Platforms"],"prefix":"10.1145","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-3638-9941","authenticated-orcid":false,"given":"Xuchao","family":"Li","sequence":"first","affiliation":[{"name":"School of Information Science and Engineering, Yanshan University, Qinhuangdao, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5496-4487","authenticated-orcid":false,"given":"Peng","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Beijing Institute of Technology, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-8908-5034","authenticated-orcid":false,"given":"Ru","family":"Ma","sequence":"additional","affiliation":[{"name":"School of Information Science and Engineering, Yanshan University, Qinhuangdao, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-7660-4445","authenticated-orcid":false,"given":"Chenghang","family":"Huo","sequence":"additional","affiliation":[{"name":"School of Information Science and Engineering, Yanshan University, Qinhuangdao, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9595-3589","authenticated-orcid":false,"given":"Fuzhi","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Information Science and Engineering, Yanshan University, Qinhuangdao, China"}]}],"member":"320","published-online":{"date-parts":[[2025,9,8]]},"reference":[{"doi-asserted-by":"publisher","key":"e_1_3_1_2_2","DOI":"10.1145\/2187836.2187863"},{"doi-asserted-by":"publisher","key":"e_1_3_1_3_2","DOI":"10.1016\/J.JNCA.2019.02.002"},{"doi-asserted-by":"publisher","key":"e_1_3_1_4_2","DOI":"10.1016\/J.JNCA.2024.104046"},{"doi-asserted-by":"publisher","key":"e_1_3_1_5_2","DOI":"10.1109\/TAI.2024.3420262"},{"doi-asserted-by":"publisher","key":"e_1_3_1_6_2","DOI":"10.1109\/TKDE.2023.3344602"},{"doi-asserted-by":"publisher","key":"e_1_3_1_7_2","DOI":"10.1016\/J.IPM.2021.102593"},{"doi-asserted-by":"publisher","key":"e_1_3_1_8_2","DOI":"10.1016\/J.KNOSYS.2020.105520"},{"doi-asserted-by":"publisher","key":"e_1_3_1_9_2","DOI":"10.1109\/ICC.2019.8761650"},{"doi-asserted-by":"publisher","key":"e_1_3_1_10_2","DOI":"10.1007\/978-3-030-00916-8_61"},{"doi-asserted-by":"publisher","key":"e_1_3_1_11_2","DOI":"10.1007\/S10115-017-1068-7"},{"doi-asserted-by":"publisher","key":"e_1_3_1_12_2","DOI":"10.1016\/J.INS.2022.05.086"},{"key":"e_1_3_1_13_2","first-page":"1","article-title":"A robust rating aggregation method based on rater group trustworthiness for collusive disturbance","author":"Zhu Huan","unstructured":"Huan Zhu, Yu Xiao, Dongmei Chen, and Jun Wu. 2024. 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