{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T21:22:40Z","timestamp":1773264160111,"version":"3.50.1"},"reference-count":29,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2026,3,9]],"date-time":"2026-03-09T00:00:00Z","timestamp":1773014400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62402124"],"award-info":[{"award-number":["62402124"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Guangxi Science and Technology Base and Talent Project","award":["GuikeAD23026154"],"award-info":[{"award-number":["GuikeAD23026154"]}]},{"name":"Guangxi Science and Technology Base and Talent Project","award":["GuikeAD23026160"],"award-info":[{"award-number":["GuikeAD23026160"]}]},{"name":"Open Project Program of Guangxi Key Laboratory of Digital Infrastructure","award":["GXDINBC202402"],"award-info":[{"award-number":["GXDINBC202402"]}]},{"name":"Open Project Program of Guangxi Key Laboratory of Digital Infrastructure","award":["GXDINBC202407"],"award-info":[{"award-number":["GXDINBC202407"]}]},{"name":"Guangxi Natural Science Foundation","award":["2025GXNSFBA069283"],"award-info":[{"award-number":["2025GXNSFBA069283"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computers"],"abstract":"<jats:p>The rapid growth of social networks and online platforms has heightened the importance of trust evaluation in various applications, including e-commerce, social networking, online collaboration, and mobile crowdsourcing. Traditional trust evaluation methods often rely on handcrafted features and simple models, which fail to fully capture the implicit patterns within the complex, heterogeneous structures of social networks. To address this issue, we propose TrustGTN, a novel method based on Heterogeneous Graph Neural Networks (HGNNs). It incorporates a soft selection mechanism that dynamically adjusts the training matrix weights. This enables it to capture the evolving structural and semantic patterns of the graph. The model can automatically learn important trust chains without the need to manually set their lengths. Experimental results show that TrustGTN outperforms existing trust evaluation methods on public datasets, demonstrating its advantages in handling heterogeneous graph data.<\/jats:p>","DOI":"10.3390\/computers15030176","type":"journal-article","created":{"date-parts":[[2026,3,9]],"date-time":"2026-03-09T08:58:45Z","timestamp":1773046725000},"page":"176","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["TrustGTN: A Social Network Trust Evaluation Method Based on Heterogeneous Graph Neural Network"],"prefix":"10.3390","volume":"15","author":[{"given":"Xiao","family":"Liu","sequence":"first","affiliation":[{"name":"Guangxi Key Laboratory of Multimedia Communications and Network Technology, Guangxi Colleges and Universities Key Laboratory of Multimedia Communications and Information Processing, School of Computer, Electronics and Information, Guangxi University, Nanning 530004, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zai","family":"Yang","sequence":"additional","affiliation":[{"name":"Guangxi Key Laboratory of Multimedia Communications and Network Technology, Guangxi Colleges and Universities Key Laboratory of Multimedia Communications and Information Processing, School of Computer, Electronics and Information, Guangxi University, Nanning 530004, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jining","family":"Chen","sequence":"additional","affiliation":[{"name":"Guangxi Key Laboratory of Digital Infrastructure, Guangxi Zhuang Autonomous Region Information Center, Nanning 530000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gaoxiang","family":"Li","sequence":"additional","affiliation":[{"name":"Guangxi Key Laboratory of Power System Optimization and Energy Technology, School of Electrical Engineering, Guangxi University, Nanning 530004, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,3,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Xie, X. (2010, January 18\u201320). Potential Friend Recommendation in Online Social Network. Proceedings of the 2010 IEEE\/ACM International Conference on Green Computing and Communications & International Conference on Cyber, Physical and Social Computing, Hangzhou, China.","DOI":"10.1109\/GreenCom-CPSCom.2010.28"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Yang, J., Wan, J., Wang, Y., and Mao, Y. (2020, January 15\u201317). Social Network-Based News Recommendation with Knowledge Graph. Proceedings of the 2020 IEEE International Conference on Information Technology, Big Data and Artificial Intelligence (ICIBA), Chongqing, China.","DOI":"10.1109\/ICIBA50161.2020.9276847"},{"key":"ref_3","unstructured":"Cheng, J., Wang, Y., Li, M., Yl\u00e4-J\u00e4\u00e4ski, A., Yu, K., and Ma, J. (2010, January 26\u201328). A New Trust Mechanism Based on Gravitation Model of Reputation Value in Social Network. Proceedings of the 2010 3rd IEEE International Conference on Broadband Network and Multimedia Technology (IC-BNMT), Beijing, China."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Kumar, A.S., and Singh, S. (2013, January 15\u201317). Detection of User Cluster with Suspicious Activity in Online Social Networking Sites. Proceedings of the 2013 2nd International Conference on Advanced Computing, Networking and Security, Mangalore, India.","DOI":"10.1109\/ADCONS.2013.17"},{"key":"ref_5","unstructured":"Yang, Z., Liu, X.-M., Yang, L., Wang, J.-X., and Ma, L.-R. (2012, January 2\u20134). A Trust Evaluation Mechanism Based on Certified Trust and Fuzzy-Derived Reputation. Proceedings of the 2012 4th International Conference on Multimedia Information Networking and Security, Nanjing, China."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Tian, L.-Q., Lin, C., and Ni, Y. (2010, January 22\u201324). Evaluation of User Behavior Trust in Cloud Computing. Proceedings of the 2010 International Conference on Computer Application and System Modeling (ICCASM 2010), Taiyuan, China.","DOI":"10.1109\/ICCASM.2010.5620636"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Nahid, T.H., Jui, F.A., and Shill, P.C. (2021, January 17\u201319). Protein Secondary Structure Prediction Using Graph Neural Network. Proceedings of the 2021 5th International Conference on Electrical Information and Communication Technology (EICT), Khulna, Bangladesh.","DOI":"10.1109\/EICT54103.2021.9733590"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Ioannidis, V.N., Marques, A.G., and Giannakis, G.B. (2019, January 15\u201318). Graph Neural Networks for Predicting Protein Functions. Proceedings of the 2019 IEEE 8th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), Le Gosier, Guadeloupe.","DOI":"10.1109\/CAMSAP45676.2019.9022646"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Awasthi, A.K., Garov, A.K., Sharma, M., and Sinha, M. (2023, January 27\u201329). GNN Model Based on Node Classification Forecasting in Social Network. Proceedings of the 2023 International Conference on Artificial Intelligence and Smart Communication (AISC), Greater Noida, India.","DOI":"10.1109\/AISC56616.2023.10085118"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"36846","DOI":"10.1109\/ACCESS.2022.3164691","article-title":"Hierarchical Structure-Feature Aware Graph Neural Network for Node Classification","volume":"10","author":"Yao","year":"2022","journal-title":"IEEE Access"},{"key":"ref_11","first-page":"181","article-title":"Contextualized Graph Attention Network for Recommendation with Item Knowledge Graph","volume":"35","author":"Liu","year":"2023","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1016\/j.dss.2015.04.009","article-title":"A Survey on Trust and Reputation Models for Web Services: Single, Composite, and Communities","volume":"74","author":"Bentahar","year":"2015","journal-title":"Decis. Support Syst."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1145\/3292499","article-title":"Trust Evaluation in Cross-Cloud Federation: Survey and Requirement Analysis","volume":"52","author":"Ahmed","year":"2020","journal-title":"ACM Comput. Surv."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"618","DOI":"10.1016\/j.dss.2005.05.019","article-title":"A Survey of Trust and Reputation Systems for Online Service Provision","volume":"43","author":"Ismail","year":"2007","journal-title":"Decis. Support Syst."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Yao, Y., Tong, H., Yan, X., Xu, F., and Lu, J. (2013, January 13\u201317). MATRI: A Multi-Aspect and Transitive Trust Inference Model. Proceedings of the 22nd International Conference on World Wide Web (WWW \u201813), Rio de Janeiro, Brazil.","DOI":"10.1145\/2488388.2488516"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Liu, G., Li, C., and Yang, Q. (May, January 29). NeuralWalk: Trust Assessment in Online Social Networks with Neural Networks. Proceedings of the IEEE INFOCOM 2019, Paris, France.","DOI":"10.1109\/INFOCOM.2019.8737469"},{"key":"ref_17","unstructured":"Kipf, T.N., and Welling, M. (2016). Semi-Supervised Classification with Graph Convolutional Networks. arXiv."},{"key":"ref_18","unstructured":"Veli\u010dkovi\u0107, P., Cucurull, G., Casanova, A., Romero, A., Li\u00f2, P., and Bengio, Y. (2017). Graph Attention Networks. arXiv."},{"key":"ref_19","unstructured":"Hamilton, W.L., Ying, R., and Leskovec, J. (2017, January 4\u20139). Inductive Representation Learning on Large Graphs. Proceedings of the Advances in Neural Information Processing Systems 30 (NeurIPS 2017), Long Beach, CA, USA."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Zhang, C., Song, D., Huang, C., Swami, A., and Chawla, N.V. (2019, January 4\u20138). Heterogeneous Graph Neural Network. Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD \u201819), Anchorage, AK, USA.","DOI":"10.1145\/3292500.3330961"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Schlichtkrull, M., Kipf, T.N., Bloem, P., van den Berg, R., Titov, I., and Welling, M. (2018, January 3\u20137). Modeling Relational Data with Graph Convolutional Networks. Proceedings of the Extended Semantic Web Conference (ESWC 2018), Heraklion, Greece.","DOI":"10.1007\/978-3-319-93417-4_38"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Fu, X., Zhang, J., Meng, Z., and King, I. (2020, January 20\u201324). MAGNN: Metapath Aggregated Graph Neural Network for Heterogeneous Graph Embedding. Proceedings of the Web Conference 2020 (WWW \u201820), Taipei, Taiwan.","DOI":"10.1145\/3366423.3380297"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Wang, X., Ji, H., Shi, C., Wang, B., Ye, Y., Cui, P., and Yu, P.S. (2019, January 13\u201317). Heterogeneous Graph Attention Network. Proceedings of the World Wide Web Conference (WWW \u201819), San Francisco, CA, USA.","DOI":"10.1145\/3308558.3313562"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Lin, W., Gao, Z., and Li, B. (2020, January 6\u20139). Guardian: Evaluating Trust in Online Social Networks with Graph Convolutional Networks. Proceedings of the IEEE INFOCOM 2020, Toronto, ON, Canada.","DOI":"10.1109\/INFOCOM41043.2020.9155370"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"14205","DOI":"10.1109\/TNNLS.2023.3275634","article-title":"TrustGNN: Graph Neural Network-Based Trust Evaluation via Learnable Propagative and Composable Nature","volume":"35","author":"Huo","year":"2024","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1016\/j.neunet.2022.05.026","article-title":"Graph Transformer Networks: Learning Meta-Path Graphs to Improve GNNs","volume":"153","author":"Yun","year":"2022","journal-title":"Neural Netw."},{"key":"ref_27","unstructured":"Sun, Z., Deng, Z., Nie, J., and Tang, J. (2019). RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Space. arXiv."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Liu, G., Chen, Q., Yang, Q., Zhu, B., Wang, H., and Wang, W. (2017, January 1\u20134). OpinionWalk: An Efficient Solution to Massive Trust Assessment in Online Social Networks. Proceedings of the IEEE INFOCOM 2017, Atlanta, GA, USA.","DOI":"10.1109\/INFOCOM.2017.8057106"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Abboud, R., Ceylan, S.L., Grohe, M., and Lukasiewicz, T. (2021, January 21\u201326). The Surprising Power of Graph Neural Networks with Random Node Initialization. Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI 2021), Virtual.","DOI":"10.24963\/ijcai.2021\/291"}],"container-title":["Computers"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-431X\/15\/3\/176\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T05:22:16Z","timestamp":1773206536000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-431X\/15\/3\/176"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,9]]},"references-count":29,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2026,3]]}},"alternative-id":["computers15030176"],"URL":"https:\/\/doi.org\/10.3390\/computers15030176","relation":{},"ISSN":["2073-431X"],"issn-type":[{"value":"2073-431X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3,9]]}}}