{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T03:31:06Z","timestamp":1773199866188,"version":"3.50.1"},"reference-count":46,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2025,3,24]],"date-time":"2025-03-24T00:00:00Z","timestamp":1742774400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,3,24]],"date-time":"2025-03-24T00:00:00Z","timestamp":1742774400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["U21A20472"],"award-info":[{"award-number":["U21A20472"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"National Key Research and Development Plan of China","award":["2021YFB3600503"],"award-info":[{"award-number":["2021YFB3600503"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Artif Intell Rev"],"DOI":"10.1007\/s10462-025-11158-1","type":"journal-article","created":{"date-parts":[[2025,3,24]],"date-time":"2025-03-24T15:05:28Z","timestamp":1742828728000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Multi-scale graph diffusion convolutional network for multi-view learning"],"prefix":"10.1007","volume":"58","author":[{"given":"Shiping","family":"Wang","sequence":"first","affiliation":[]},{"given":"Jiacheng","family":"Li","sequence":"additional","affiliation":[]},{"given":"Yuhong","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Zhihao","family":"Wu","sequence":"additional","affiliation":[]},{"given":"Aiping","family":"Huang","sequence":"additional","affiliation":[]},{"given":"Le","family":"Zhang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,3,24]]},"reference":[{"issue":"3","key":"11158_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3441450","volume":"15","author":"SK Ata","year":"2021","unstructured":"Ata SK, Fang Y, Wu M et al (2021) Multi-view collaborative network embedding. ACM Trans Knowl Discov Data 15(3):1\u201318","journal-title":"ACM Trans Knowl Discov Data"},{"key":"11158_CR2","unstructured":"Chen M, Wei Z, Huang Z, et\u00a0al (2020) Simple and deep graph convolutional networks. In: Proceedings of the 37th international conference on machine learning, p 1725\u20131735"},{"key":"11158_CR44","doi-asserted-by":"crossref","unstructured":"Chen Y, Chen F, Wu Z, et al (2025) Heterogeneous graph embedding with dual edge differentiation. Neural Netw 183:106965","DOI":"10.1016\/j.neunet.2024.106965"},{"key":"11158_CR45","unstructured":"Chen Y, Song A, Yin H, et al (2024) Multi-view incremental learning with structured hebbian plasticity for enhanced fusion efficiency. In: Proceedings of the 35th AAAI conference on artificial intelligence, p 1\u201311"},{"key":"11158_CR3","doi-asserted-by":"publisher","first-page":"161","DOI":"10.1016\/j.neunet.2023.09.006","volume":"168","author":"Y Chen","year":"2023","unstructured":"Chen Y, Wu Z, Chen Z et al (2023a) Joint learning of feature and topology for multi-view graph convolutional network. Neural Netw 168:161\u2013170","journal-title":"Neural Netw"},{"key":"11158_CR4","doi-asserted-by":"publisher","first-page":"109","DOI":"10.1016\/j.inffus.2023.02.013","volume":"95","author":"Z Chen","year":"2023","unstructured":"Chen Z, Fu L, Yao J et al (2023b) Learnable graph convolutional network and feature fusion for multi-view learning. Inform Fusion 95:109\u2013119","journal-title":"Inform Fusion"},{"key":"11158_CR5","doi-asserted-by":"crossref","unstructured":"Derr T, Ma Y, Tang J (2018) Signed graph convolutional networks. In: Proceedings of the 18th IEEE international conference on data mining, p 929\u2013934","DOI":"10.1109\/ICDM.2018.00113"},{"key":"11158_CR6","doi-asserted-by":"crossref","unstructured":"Feng W, Sheng G, Wang Q, et\u00a0al (2024) Partial multi-view clustering via self-supervised network. In: Proceedings of the AAAI conference on artificial intelligence, p 11988\u201311995","DOI":"10.1609\/aaai.v38i11.29086"},{"key":"11158_CR7","doi-asserted-by":"crossref","unstructured":"Fu D, Xu Z, Li B, et\u00a0al (2020) A view-adversarial framework for multi-view network embedding. In: Proceedings of the 29th ACM international conference on information & knowledge management, p 2025\u20132028","DOI":"10.1145\/3340531.3412127"},{"key":"11158_CR8","unstructured":"Han Z, Zhang C, Fu H, et\u00a0al (2021) Trusted multi-view classification. In: Proceedings of the 9th international conference on learning representations, p 1\u201313"},{"key":"11158_CR10","doi-asserted-by":"publisher","first-page":"6997","DOI":"10.1109\/TIP.2021.3101917","volume":"30","author":"A Huang","year":"2021","unstructured":"Huang A, Wang Z, Zheng Y et al (2021) Embedding regularizer learning for multi-view semi-supervised classification. IEEE Trans Image Proc 30:6997\u20137011","journal-title":"IEEE Trans Image Proc"},{"key":"11158_CR9","doi-asserted-by":"crossref","unstructured":"Huang J, Yang J (2021) UniGNN: a unified framework for graph and hypergraph neural networks. In: Proceedings of the 30th international joint conference on artificial intelligence, p 2563\u20132569","DOI":"10.24963\/ijcai.2021\/353"},{"key":"11158_CR11","unstructured":"Javaloy A, Sanchez-Martin P, Levi A, et\u00a0al (2022) Learnable graph convolutional attention networks. In: Proceedings of the 11th international conference on learning representations, p 1\u201335"},{"key":"11158_CR12","doi-asserted-by":"publisher","first-page":"204","DOI":"10.1016\/j.neunet.2022.05.024","volume":"153","author":"B Jiang","year":"2022","unstructured":"Jiang B, Chen S, Wang B et al (2022) Mglnn: semi-supervised learning via multiple graph cooperative learning neural networks. Neural Netw 153:204\u2013214","journal-title":"Neural Netw"},{"key":"11158_CR13","doi-asserted-by":"publisher","first-page":"3615","DOI":"10.1109\/TNNLS.2022.3194957","volume":"35","author":"B Jiang","year":"2022","unstructured":"Jiang B, Wu X, Zhou X et al (2022) Semi-supervised multiview feature selection with adaptive graph learning. IEEE Trans Neural Netw Learn Syst 35:3615\u20133629","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"11158_CR14","unstructured":"Kipf TN, Welling M (2017) Semi-supervised classification with graph convolutional networks. In: Proceedings of the 5th international conference on learning representations, p 1\u201313"},{"key":"11158_CR46","doi-asserted-by":"crossref","unstructured":"Lian J, Wang X, Lin X, Wu Z, Wang S, Guo W (2024) Graph anomaly detection via multi-view discriminative awareness learning. IEEE Trans Netw Sci Eng 11:6623\u20136635","DOI":"10.1109\/TNSE.2024.3462462"},{"key":"11158_CR17","doi-asserted-by":"crossref","unstructured":"Liang W, Liu X, Zhou S, et\u00a0al (2022) Robust graph-based multi-view clustering. In: Proceedings of the 36th AAAI conference on artificial intelligence, p 7462\u20137469","DOI":"10.1609\/aaai.v36i7.20710"},{"key":"11158_CR15","doi-asserted-by":"crossref","unstructured":"Li Q, Han Z, Wu XM (2018) Deeper insights into graph convolutional networks for semi-supervised learning. In: Proceedings of the 32nd AAAI conference on artificial intelligence, p 3538\u20133545","DOI":"10.1609\/aaai.v32i1.11604"},{"key":"11158_CR16","doi-asserted-by":"crossref","unstructured":"Li S, Li WT, Wang W (2020) Co-gcn for multi-view semi-supervised learning. In: Proceedings of the 34th AAAI conference on artificial intelligence, p 4691\u20134698","DOI":"10.1609\/aaai.v34i04.5901"},{"key":"11158_CR19","doi-asserted-by":"crossref","unstructured":"Liu M, Gao H, Ji S (2020) Towards deeper graph neural networks. In: Proceedings of the 26th ACM SIGKDD international conference on knowledge discovery & data mining, p 338\u2013348","DOI":"10.1145\/3394486.3403076"},{"issue":"10","key":"11158_CR18","doi-asserted-by":"publisher","first-page":"2410","DOI":"10.1109\/TPAMI.2018.2879108","volume":"41","author":"X Liu","year":"2018","unstructured":"Liu X, Zhu X, Li M et al (2018) Late fusion incomplete multi-view clustering. IEEE Trans Pattern Anal Mach Intell 41(10):2410\u20132423","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"11158_CR20","doi-asserted-by":"crossref","unstructured":"Lu J, Wu Z, Zhong L, et\u00a0al (2024) Generative essential graph convolutional network for multi-view semi-supervised classification. IEEE transactions on multimedia","DOI":"10.1109\/TMM.2024.3374579"},{"key":"11158_CR21","unstructured":"Peng H, Ran R, Luo Y, et\u00a0al (2024) Lingcn: structural linearized graph convolutional network for homomorphically encrypted inference. In: Proceedings of the 38th annual conference on neural information processing systems, p 1\u201316"},{"issue":"5500","key":"11158_CR22","doi-asserted-by":"publisher","first-page":"2323","DOI":"10.1126\/science.290.5500.2323","volume":"290","author":"ST Roweis","year":"2000","unstructured":"Roweis ST, Saul LK (2000) Nonlinear dimensionality reduction by locally linear embedding. Science 290(5500):2323\u20132326","journal-title":"Science"},{"key":"11158_CR23","doi-asserted-by":"crossref","unstructured":"Shao W, He L, Lu CT, et\u00a0al (2016) Online unsupervised multi-view feature selection. In: Proceedings of the 16th international conference on data mining, p 1203\u20131208","DOI":"10.1109\/ICDM.2016.0160"},{"key":"11158_CR24","doi-asserted-by":"publisher","first-page":"107102","DOI":"10.1016\/j.neunet.2024.107102","volume":"184","author":"Y Shi","year":"2024","unstructured":"Shi Y, Pi Y, Liu Z et al (2024) Information-controlled graph convolutional network for multi-view semi-supervised classification. Neural Netw 184:107102","journal-title":"Neural Netw"},{"key":"11158_CR26","doi-asserted-by":"publisher","first-page":"333","DOI":"10.1016\/j.neunet.2023.05.019","volume":"165","author":"K Sun","year":"2023","unstructured":"Sun K, Tang C, Tang C et al (2023) Multi-view subspace clustering via adaptive graph learning and late fusion alignment. Neural Netw 165:333\u2013343","journal-title":"Neural Netw"},{"key":"11158_CR25","doi-asserted-by":"crossref","unstructured":"Sun Y, Wang S, Hsieh TY, et\u00a0al (2019) Megan: a generative adversarial network for multi-view network embedding. In: Proceedings of the 28th international joint conference on artificial intelligence, p 3527\u20133533","DOI":"10.24963\/ijcai.2019\/489"},{"issue":"9","key":"11158_CR27","doi-asserted-by":"publisher","first-page":"4283","DOI":"10.1109\/TIP.2017.2717191","volume":"26","author":"H Tao","year":"2017","unstructured":"Tao H, Hou C, Nie F et al (2017) Scalable multi-view semi-supervised classification via adaptive regression. IEEE Trans Image Proc 26(9):4283\u20134296","journal-title":"IEEE Trans Image Proc"},{"key":"11158_CR28","unstructured":"Veli\u010dkovi\u0107 P, Cucurull G, Casanova A, et\u00a0al (2017) Graph attention networks. In: Proceedings of the 6th international conference on learning representations, p 1\u201312"},{"key":"11158_CR29","doi-asserted-by":"publisher","first-page":"90","DOI":"10.1016\/j.neunet.2021.10.008","volume":"145","author":"V Verma","year":"2022","unstructured":"Verma V, Kawaguchi K, Lamb A et al (2022) Interpolation consistency training for semi-supervised learning. Neural Netw 145:90\u2013106","journal-title":"Neural Netw"},{"issue":"9","key":"11158_CR31","doi-asserted-by":"crossref","first-page":"5042","DOI":"10.1109\/TPAMI.2021.3072422","volume":"44","author":"S Wang","year":"2022","unstructured":"Wang S, Chen Z, Du S et al (2022) Learning deep sparse regularizers with applications to multi-view clustering and semi-supervised classification. IEEE Trans Pattern Anal Mach Intell 44(9):5042\u20135055","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"11158_CR30","unstructured":"Wang Y, Wang Y, Yang J, et\u00a0al (2021) Dissecting the diffusion process in linear graph convolutional networks. In: Proceedings of the 34th annual conference on neural information processing systems, p 5758\u20135769"},{"key":"11158_CR32","unstructured":"Wu F, Jr. AHS, Zhang T, et\u00a0al (2019) Simplifying graph convolutional networks. In: Proceedings of the 36th international conference on machine learning, p 6861\u20136871"},{"key":"11158_CR34","doi-asserted-by":"publisher","first-page":"93","DOI":"10.1016\/j.neunet.2023.01.037","volume":"161","author":"D Xie","year":"2023","unstructured":"Xie D, Gao Q, Yang M (2023) Enhanced tensor low-rank representation learning for multi-view clustering. Neural Netw 161:93\u2013104","journal-title":"Neural Netw"},{"issue":"2","key":"11158_CR33","doi-asserted-by":"publisher","first-page":"572","DOI":"10.1109\/TCYB.2018.2869789","volume":"50","author":"Y Xie","year":"2018","unstructured":"Xie Y, Zhang W, Qu Y et al (2018) Hyper-laplacian regularized multilinear multiview self-representations for clustering and semisupervised learning. IEEE Trans Cybern 50(2):572\u2013586","journal-title":"IEEE Trans Cybern"},{"key":"11158_CR36","doi-asserted-by":"crossref","unstructured":"Xu C, Si J, Guan Z, et\u00a0al (2024) Reliable conflictive multi-view learning. In: Proceedings of the 35th AAAI conference on artificial intelligence, p 16129\u201316137","DOI":"10.1609\/aaai.v38i14.29546"},{"key":"11158_CR35","doi-asserted-by":"crossref","unstructured":"Xu J, Li W, Liu X, et\u00a0al (2020) Deep embedded complementary and interactive information for multi-view classification. In: Proceedings of the 34th AAAI conference on artificial intelligence, p 6494\u20136501","DOI":"10.1609\/aaai.v34i04.6122"},{"key":"11158_CR37","doi-asserted-by":"publisher","first-page":"236","DOI":"10.1016\/j.patcog.2018.11.015","volume":"88","author":"M Yang","year":"2019","unstructured":"Yang M, Deng C, Nie F (2019) Adaptive-weighting discriminative regression for multi-view classification. Pattern Recogn 88:236\u2013245","journal-title":"Pattern Recogn"},{"key":"11158_CR38","doi-asserted-by":"publisher","first-page":"38","DOI":"10.1016\/j.neunet.2023.06.045","volume":"166","author":"B Yu","year":"2023","unstructured":"Yu B, Xie C, Tang P et al (2023) Multi-view graph representation with similarity diffusion for general zero-shot learning. Neural Netw 166:38\u201350","journal-title":"Neural Netw"},{"key":"11158_CR39","doi-asserted-by":"publisher","first-page":"137","DOI":"10.1016\/j.neunet.2023.06.038","volume":"166","author":"Y Yun","year":"2023","unstructured":"Yun Y, Li J, Gao Q et al (2023) Low-rank discrete multi-view spectral clustering. Neural Netw 166:137\u2013147","journal-title":"Neural Netw"},{"key":"11158_CR40","doi-asserted-by":"crossref","unstructured":"Zhang L, Yan X, He J, et\u00a0al (2023) Drgcn: dynamic evolving initial residual for deep graph convolutional networks. In: Proceedings of the 34th AAAI conference on artificial intelligence, p 1\u20138","DOI":"10.1609\/aaai.v37i9.26332"},{"key":"11158_CR41","unstructured":"Zhao J, Dong Y, Ding M, et\u00a0al (2021) Adaptive diffusion in graph neural networks. In: Proceedings of the 34th annual conference on neural information processing systems, p 23321\u201323333"},{"key":"11158_CR42","doi-asserted-by":"publisher","first-page":"174","DOI":"10.1016\/j.neunet.2021.11.015","volume":"146","author":"N Ziraki","year":"2022","unstructured":"Ziraki N, Dornaika F, Bosaghzadeh A (2022) Multiple-view flexible semi-supervised classification through consistent graph construction and label propagation. Neural Netw 146:174\u2013180","journal-title":"Neural Netw"},{"key":"11158_CR43","doi-asserted-by":"publisher","first-page":"496","DOI":"10.1016\/j.neunet.2023.10.052","volume":"169","author":"Y Zou","year":"2024","unstructured":"Zou Y, Fang Z, Wu Z et al (2024) Revisiting multi-view learning: a perspective of implicitly heterogeneous graph convolutional network. Neural Netw 169:496\u2013505","journal-title":"Neural Netw"}],"container-title":["Artificial Intelligence Review"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10462-025-11158-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10462-025-11158-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10462-025-11158-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,17]],"date-time":"2025-04-17T19:33:41Z","timestamp":1744918421000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10462-025-11158-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,3,24]]},"references-count":46,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2025,6]]}},"alternative-id":["11158"],"URL":"https:\/\/doi.org\/10.1007\/s10462-025-11158-1","relation":{},"ISSN":["1573-7462"],"issn-type":[{"value":"1573-7462","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,3,24]]},"assertion":[{"value":"18 February 2025","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 March 2025","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no Conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"184"}}