{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,10]],"date-time":"2026-07-10T04:15:19Z","timestamp":1783656919843,"version":"3.55.0"},"reference-count":54,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Knowledge-Based Systems"],"published-print":{"date-parts":[[2026,9]]},"DOI":"10.1016\/j.knosys.2026.116439","type":"journal-article","created":{"date-parts":[[2026,6,18]],"date-time":"2026-06-18T16:40:01Z","timestamp":1781800801000},"page":"116439","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Reliability-aware dual graph convolutional network for multi-view learning"],"prefix":"10.1016","volume":"350","author":[{"given":"Hongrong","family":"Chen","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weiran","family":"Liao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jielong","family":"Lu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fu","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiayuan","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haiyao","family":"Su","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1672-3065","authenticated-orcid":false,"given":"Na","family":"Song","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5195-9682","authenticated-orcid":false,"given":"Shiping","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.knosys.2026.116439_b1","doi-asserted-by":"crossref","first-page":"127764","DOI":"10.1016\/j.eswa.2025.127764","article-title":"Multi-view aggregation and multi-relation alignment for few-shot fine-grained recognition","volume":"283","author":"Chen","year":"2025","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.knosys.2026.116439_b2","doi-asserted-by":"crossref","first-page":"617","DOI":"10.1109\/TIP.2019.2934576","article-title":"Multi-view image classification with visual, semantic and view consistency","volume":"29","author":"Zhang","year":"2020","journal-title":"IEEE Trans. Image Process."},{"key":"10.1016\/j.knosys.2026.116439_b3","doi-asserted-by":"crossref","unstructured":"Man-Sheng Chen, Ling Huang, Chang-Dong Wang, Dong Huang, Multi-View Clustering in Latent Embedding Space, in: Proceedings of the AAAI Conference on Artificial Intelligence, 2020, pp. 3513\u20133520.","DOI":"10.1609\/aaai.v34i04.5756"},{"key":"10.1016\/j.knosys.2026.116439_b4","doi-asserted-by":"crossref","first-page":"8265","DOI":"10.1109\/TIP.2021.3113791","article-title":"Multi-modal interaction graph convolutional network for temporal language localization in videos","volume":"30","author":"Zhang","year":"2021","journal-title":"IEEE Trans. Image Process."},{"key":"10.1016\/j.knosys.2026.116439_b5","doi-asserted-by":"crossref","first-page":"111159","DOI":"10.1016\/j.patcog.2024.111159","article-title":"Semi-supervised multi-view feature selection with adaptive similarity fusion and learning","volume":"159","author":"Jiang","year":"2025","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.knosys.2026.116439_b6","doi-asserted-by":"crossref","first-page":"110355","DOI":"10.1016\/j.patcog.2024.110355","article-title":"Robust multi-view learning via M-estimator joint sparse representation","volume":"151","author":"Hu","year":"2024","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.knosys.2026.116439_b7","doi-asserted-by":"crossref","unstructured":"Daoyuan Li, Zuyuan Yang, Shengli Xie, FedMSGL: A Self-Expressive Hypergraph Based Federated Multi-View Learning, in: Proceedings of the AAAI Conference on Artificial Intelligence, 2025, pp. 18244\u201318252.","DOI":"10.1609\/aaai.v39i17.34007"},{"key":"10.1016\/j.knosys.2026.116439_b8","series-title":"A survey on multi-view learning","author":"Xu","year":"2013"},{"key":"10.1016\/j.knosys.2026.116439_b9","doi-asserted-by":"crossref","first-page":"197334","DOI":"10.1007\/s11704-024-40004-w","article-title":"A review on multi-view learning","volume":"19","author":"Yu","year":"2025","journal-title":"Front. Comput. Sci."},{"key":"10.1016\/j.knosys.2026.116439_b10","doi-asserted-by":"crossref","first-page":"1655","DOI":"10.1109\/TCYB.2018.2883673","article-title":"Multiview latent space learning with feature redundancy minimization","volume":"50","author":"Zhou","year":"2018","journal-title":"IEEE Trans. Cybern."},{"key":"10.1016\/j.knosys.2026.116439_b11","doi-asserted-by":"crossref","first-page":"1863","DOI":"10.1109\/TKDE.2018.2872063","article-title":"A survey of multi-view representation learning","volume":"31","author":"Li","year":"2018","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"10.1016\/j.knosys.2026.116439_b12","doi-asserted-by":"crossref","first-page":"6641","DOI":"10.1109\/TKDE.2024.3399738","article-title":"Multiple kernel clustering with adaptive multi-scale partition selection","volume":"36","author":"Wang","year":"2024","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"10.1016\/j.knosys.2026.116439_b13","doi-asserted-by":"crossref","first-page":"1412","DOI":"10.1109\/TPAMI.2021.3116948","article-title":"Incomplete multiple kernel alignment maximization for clustering","volume":"46","author":"Liu","year":"2024","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.knosys.2026.116439_b14","doi-asserted-by":"crossref","first-page":"119458","DOI":"10.1016\/j.eswa.2022.119458","article-title":"Deep probability multi-view feature learning for data clustering","volume":"217","author":"Zhao","year":"2023","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.knosys.2026.116439_b15","unstructured":"Diederik P. Kingma, Max Welling, Auto-Encoding Variational Bayes, in: Proceedings of the International Conference on Learning Representations, 2014, pp. 1\u201314."},{"key":"10.1016\/j.knosys.2026.116439_b16","doi-asserted-by":"crossref","unstructured":"Zhiwei Li, Guodong Long, Tianyi Zhou, Jing Jiang, Chengqi Zhang, Personalized Federated Collaborative Filtering: A Variational AutoEncoder Approach, in: Proceedings of the AAAI Conference on Artificial Intelligence, 2025, pp. 18602\u201318610.","DOI":"10.1609\/aaai.v39i17.34047"},{"key":"10.1016\/j.knosys.2026.116439_b17","doi-asserted-by":"crossref","unstructured":"Hao Wang, Zhi-Qi Cheng, Jingdong Sun, Xin Yang, Xiao Wu, Hongyang Chen, Yan Yang, Debunking Free Fusion Myth: Online Multi-view Anomaly Detection with Disentangled Product-of-Experts Modeling, in: Proceedings of the ACM International Conference on Multimedia, 2023, pp. 3277\u20133286.","DOI":"10.1145\/3581783.3612487"},{"key":"10.1016\/j.knosys.2026.116439_b18","unstructured":"Yuge Shi, N. Siddharth, Brooks Paige, Philip H. S. Torr, Variational mixture-of-experts autoencoders for multi-modal deep generative models, in: Proceedings of the International Conference on Neural Information Processing Systems, 2019, pp. 1\u201312."},{"key":"10.1016\/j.knosys.2026.116439_b19","doi-asserted-by":"crossref","first-page":"132102","DOI":"10.1007\/s11432-023-3939-1","article-title":"Unsupervised multiplex graph diffusion networks with multi-level canonical correlation analysis for multiplex graph representation learning","volume":"68","author":"Fu","year":"2025","journal-title":"Sci. China Inf. Sci."},{"key":"10.1016\/j.knosys.2026.116439_b20","doi-asserted-by":"crossref","first-page":"1904","DOI":"10.1109\/TPAMI.2025.3618984","article-title":"Probabilistically aligned view-unaligned clustering with adaptive template selection","volume":"48","author":"Dong","year":"2026","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"9","key":"10.1016\/j.knosys.2026.116439_b21","doi-asserted-by":"crossref","first-page":"2281","DOI":"10.1109\/TPAMI.2017.2749576","article-title":"Sharable and individual multi-view metric learning","volume":"40","author":"Hu","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.knosys.2026.116439_b22","doi-asserted-by":"crossref","unstructured":"Mihee Lee, Vladimir Pavlovic, Private-Shared Disentangled Multimodal VAE for Learning of Latent Representations, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2021, pp. 1692\u20131700.","DOI":"10.1109\/CVPRW53098.2021.00185"},{"key":"10.1016\/j.knosys.2026.116439_b23","first-page":"15216","article-title":"Beyond local patterns: Multiscale inconsistency learning for graph anomaly detection","volume":"vol. 40","author":"Lian","year":"2026"},{"key":"10.1016\/j.knosys.2026.116439_b24","doi-asserted-by":"crossref","unstructured":"Shu Li, Wen-Tao Li, Wei Wang, Co-GCN for Multi-View Semi-Supervised Learning, in: Proceedings of the AAAI Conference on Artificial Intelligence, 2020, pp. 4691\u20134698.","DOI":"10.1609\/aaai.v34i04.5901"},{"issue":"6","key":"10.1016\/j.knosys.2026.116439_b25","doi-asserted-by":"crossref","first-page":"6623","DOI":"10.1109\/TNSE.2024.3462462","article-title":"Graph anomaly detection via multi-view discriminative awareness learning","volume":"11","author":"Lian","year":"2024","journal-title":"IEEE Trans. Netw. Sci. Eng."},{"key":"10.1016\/j.knosys.2026.116439_b26","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2020.101766","article-title":"Uncertainty-aware multi-view co-training for semi-supervised medical image segmentation and domain adaptation","volume":"65","author":"Xia","year":"2020","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.knosys.2026.116439_b27","first-page":"7545","article-title":"Uncertainty-aware multi-view representation learning","volume":"vol. 35","author":"Geng","year":"2021"},{"key":"10.1016\/j.knosys.2026.116439_b28","first-page":"19314","article-title":"Iterative deep graph learning for graph neural networks: Better and robust node embeddings","volume":"33","author":"Chen","year":"2020","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.knosys.2026.116439_b29","unstructured":"Thomas N. Kipf, Max Welling, Semi-supervised classification with graph convolutional networks, in: Proceedings of the International Conference on Learning Representations, 2017, pp. 1\u201313."},{"key":"10.1016\/j.knosys.2026.116439_b30","doi-asserted-by":"crossref","first-page":"121708","DOI":"10.1016\/j.ins.2024.121708","article-title":"Robust graph mutual-assistance convolutional networks for semi-supervised node classification tasks","volume":"694","author":"Guo","year":"2025","journal-title":"Inform. Sci."},{"key":"10.1016\/j.knosys.2026.116439_b31","doi-asserted-by":"crossref","first-page":"107693","DOI":"10.1016\/j.neunet.2025.107693","article-title":"Multi-scale signed graph convolutional network based on framelet","volume":"191","author":"Chu","year":"2025","journal-title":"Neural Netw."},{"key":"10.1016\/j.knosys.2026.116439_b32","doi-asserted-by":"crossref","unstructured":"Xin He, Yili Wang, Wenqi Fan, Xu Shen, Xin Juan, Rui Miao, Xin Wang, Mamba-Based Graph Convolutional Networks: Tackling Over-smoothing with Selective State Space, in: Proceedings of the International Joint Conference on Artificial Intelligence, 2025, pp. 5345\u20135353.","DOI":"10.24963\/ijcai.2025\/595"},{"key":"10.1016\/j.knosys.2026.116439_b33","unstructured":"Mike Wu, Noah Goodman, Multimodal generative models for scalable weakly-supervised learning, in: Proceedings of the International Conference on Neural Information Processing Systems, 2018, pp. 5580\u20135590."},{"key":"10.1016\/j.knosys.2026.116439_b34","unstructured":"Masahiro Suzuki, Kotaro Nakayama, Yutaka Matsuo, Joint Multimodal Learning with Deep Generative Models, in: Proceedings of International Conference on Learning Representations, 2017, pp. 1\u201312."},{"key":"10.1016\/j.knosys.2026.116439_b35","doi-asserted-by":"crossref","unstructured":"Jie Xu, Yazhou Ren, Huayi Tang, Xiaorong Pu, Xiaofeng Zhu, Ming Zeng, Lifang He, Multi-VAE: Learning Disentangled View-common and View-peculiar Visual Representations for Multi-view Clustering, in: IEEE\/CVF International Conference on Computer Vision, 2021, pp. 9214\u20139223.","DOI":"10.1109\/ICCV48922.2021.00910"},{"key":"10.1016\/j.knosys.2026.116439_b36","doi-asserted-by":"crossref","unstructured":"Xinke Jiang, Zidi Qin, Jiarong Xu, Xiang Ao, Incomplete Graph Learning via Attribute-Structure Decoupled Variational Auto-Encoder, in: Proceedings of the ACM International Conference on Web Search and Data Mining, 2024, pp. 304\u2013312.","DOI":"10.1145\/3616855.3635769"},{"key":"10.1016\/j.knosys.2026.116439_b37","doi-asserted-by":"crossref","first-page":"2314","DOI":"10.1109\/TAI.2025.3545396","article-title":"ITF-VAE: Variational auto-encoder using interpretable continuous time series features","volume":"6","author":"Klopries","year":"2025","journal-title":"IEEE Trans. Artif. Intell."},{"key":"10.1016\/j.knosys.2026.116439_b38","first-page":"1","article-title":"SCAE: structural contrastive auto-encoder for incomplete multi-view representation learning","volume":"20","author":"Li","year":"2024","journal-title":"ACM Trans. Multimed. Comput. Commun. Appl."},{"key":"10.1016\/j.knosys.2026.116439_b39","doi-asserted-by":"crossref","unstructured":"Cai Xu, Jiajun Si, Ziyu Guan, Wei Zhao, Yue Wu, Xiyue Gao, Reliable Conflictive Multi-View Learning, in: Proceedings of the AAAI Conference on Artificial Intelligence, 2024, pp. 16129\u201316137.","DOI":"10.1609\/aaai.v38i14.29546"},{"key":"10.1016\/j.knosys.2026.116439_b40","doi-asserted-by":"crossref","unstructured":"Penghang Yu, Zhiyi Tan, Guanming Lu, Bing-Kun Bao, Multi-View Graph Convolutional Network for Multimedia Recommendation, in: Proceedings of the ACM International Conference on Multimedia, 2023, pp. 6576\u20136585.","DOI":"10.1145\/3581783.3613915"},{"key":"10.1016\/j.knosys.2026.116439_b41","doi-asserted-by":"crossref","unstructured":"Lecheng Zheng, Yu Cheng, Hongxia Yang, Nan Cao, Jingrui He, Deep Co-Attention Network for Multi-View Subspace Learning, in: Proceedings of the Web Conference, 2021, pp. 1528\u20131539.","DOI":"10.1145\/3442381.3449801"},{"key":"10.1016\/j.knosys.2026.116439_b42","doi-asserted-by":"crossref","unstructured":"Gengyu Lyu, Xiang Deng, Yanan Wu, Songhe Feng, Beyond Shared Subspace: A View-Specific Fusion for Multi-View Multi-Label Learning, in: Proceedings of the AAAI Conference on Artificial Intelligence, 2022, pp. 7647\u20137654.","DOI":"10.1609\/aaai.v36i7.20731"},{"key":"10.1016\/j.knosys.2026.116439_b43","doi-asserted-by":"crossref","first-page":"107105","DOI":"10.1016\/j.neunet.2024.107105","article-title":"Contrastive independent subspace analysis network for multi-view spatial information extraction","volume":"185","author":"Zhang","year":"2025","journal-title":"Neural Netw."},{"key":"10.1016\/j.knosys.2026.116439_b44","doi-asserted-by":"crossref","unstructured":"Wulin Xie, Xiaohuan Lu, Yadong Liu, Jiang Long, Bob Zhang, Shuping Zhao, Jie Wen, Uncertainty-Aware Pseudo-Labeling and Dual Graph Driven Network for Incomplete Multi-View Multi-Label Classification, in: Proceedings of the ACM International Conference on Multimedia, 2024, pp. 6656\u20136665.","DOI":"10.1145\/3664647.3680932"},{"key":"10.1016\/j.knosys.2026.116439_b45","doi-asserted-by":"crossref","unstructured":"Jielong Lu, Zhihao Wu, Zhaoliang Chen, Zhiling Cai, Shiping Wang, Towards Multi-view Consistent Graph Diffusion, in: Proceedings of the ACM International Conference on Multimedia, 2024, pp. 186\u2013195.","DOI":"10.1145\/3664647.3681258"},{"key":"10.1016\/j.knosys.2026.116439_b46","doi-asserted-by":"crossref","unstructured":"Haojian Huang, Chuanyu Qin, Zhe Liu, Kaijing Ma, Jin Chen, Han Fang, Chao Ban, Hao Sun, Zhongjiang He, Trusted unified feature-neighborhood dynamics for multi-view classification, in: Proceedings of the AAAI Conference on Artificial Intelligence, 2025, pp. 17413\u201317421.","DOI":"10.1609\/aaai.v39i16.33914"},{"key":"10.1016\/j.knosys.2026.116439_b47","doi-asserted-by":"crossref","first-page":"102098","DOI":"10.1016\/j.inffus.2023.102098","article-title":"Knowledge distillation-driven semi-supervised multi-view classification","volume":"103","author":"Wang","year":"2024","journal-title":"Inf. Fusion"},{"key":"10.1016\/j.knosys.2026.116439_b48","doi-asserted-by":"crossref","unstructured":"Wei Liu, Yufei Chen, Xiaodong Yue, Enhancing Multi-View Classification Reliability with Adaptive Rejection, in: Proceedings of the AAAI Conference on Artificial Intelligence, 2025, pp. 18969\u201318977.","DOI":"10.1609\/aaai.v39i18.34088"},{"key":"10.1016\/j.knosys.2026.116439_b49","unstructured":"Irina Higgins, Lo\u00efc Matthey, Arka Pal, Christopher P. Burgess, Xavier Glorot, Matthew M. Botvinick, Shakir Mohamed, Alexander Lerchner, beta-VAE: Learning Basic Visual Concepts with a Constrained Variational Framework, in: Proceedings of the International Conference on Learning Representations, 2017, pp. 1\u201322."},{"key":"10.1016\/j.knosys.2026.116439_b50","first-page":"5042","article-title":"Learning deep sparse regularizers with applications to multi-view clustering and semi-supervised classification","volume":"44","author":"Wang","year":"2022","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.knosys.2026.116439_b51","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1016\/j.inffus.2023.02.013","article-title":"Learnable graph convolutional network and feature fusion for multi-view learning","volume":"95","author":"Chen","year":"2023","journal-title":"Inf. Fusion"},{"key":"10.1016\/j.knosys.2026.116439_b52","doi-asserted-by":"crossref","first-page":"8593","DOI":"10.1109\/TMM.2023.3260649","article-title":"Interpretable graph convolutional network for multi-view semi-supervised learning","volume":"25","author":"Wu","year":"2023","journal-title":"IEEE Trans. Multimed."},{"key":"10.1016\/j.knosys.2026.116439_b53","doi-asserted-by":"crossref","first-page":"7987","DOI":"10.1109\/TMM.2024.3374579","article-title":"Generative essential graph convolutional network for multi-view semi-supervised classification","volume":"26","author":"Lu","year":"2024","journal-title":"IEEE Trans. Multimed."},{"key":"10.1016\/j.knosys.2026.116439_b54","doi-asserted-by":"crossref","first-page":"8889","DOI":"10.1109\/TMM.2024.3383295","article-title":"Representation learning meets optimization-derived networks: From single-view to multi-view","volume":"26","author":"Fang","year":"2024","journal-title":"IEEE Trans. Multimed."}],"container-title":["Knowledge-Based Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0950705126011652?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0950705126011652?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,7,10]],"date-time":"2026-07-10T03:41:21Z","timestamp":1783654881000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0950705126011652"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9]]},"references-count":54,"alternative-id":["S0950705126011652"],"URL":"https:\/\/doi.org\/10.1016\/j.knosys.2026.116439","relation":{},"ISSN":["0950-7051"],"issn-type":[{"value":"0950-7051","type":"print"}],"subject":[],"published":{"date-parts":[[2026,9]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Reliability-aware dual graph convolutional network for multi-view learning","name":"articletitle","label":"Article Title"},{"value":"Knowledge-Based Systems","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.knosys.2026.116439","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"116439"}}