{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,8]],"date-time":"2026-07-08T13:40:07Z","timestamp":1783518007022,"version":"3.55.0"},"reference-count":51,"publisher":"Association for Computing Machinery (ACM)","issue":"1","license":[{"start":{"date-parts":[[2023,8,10]],"date-time":"2023-08-10T00:00:00Z","timestamp":1691625600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["U21A20472 and 62276065"],"award-info":[{"award-number":["U21A20472 and 62276065"]}],"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":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Knowl. Discov. Data"],"published-print":{"date-parts":[[2024,1,31]]},"abstract":"<jats:p>\n            Multi-view data containing complementary and consensus information can facilitate representation learning by exploiting the intact integration of multi-view features. Because most objects in the real world often have underlying connections, organizing multi-view data as heterogeneous graphs is beneficial to extracting latent information among different objects. Due to the powerful capability to gather information of neighborhood nodes, in this article, we apply Graph Convolutional Network (GCN) to cope with heterogeneous graph data originating from multi-view data, which is still under-explored in the field of GCN. In order to improve the quality of network topology and alleviate the interference of noises yielded by graph fusion, some methods undertake sorting operations before the graph convolution procedure. These GCN-based methods generally sort and select the most confident neighborhood nodes for each vertex, such as picking the top-\n            <jats:italic>k<\/jats:italic>\n            nodes according to pre-defined confidence values. Nonetheless, this is problematic due to the non-differentiable sorting operators and inflexible graph embedding learning, which may result in blocked gradient computations and undesired performance. To cope with these issues, we propose a joint framework dubbed Multi-view Graph Convolutional Network with Differentiable Node Selection (MGCN-DNS), which is constituted of an adaptive graph fusion layer, a graph learning module, and a differentiable node selection schema. MGCN-DNS accepts multi-channel graph-structural data as inputs and aims to learn more robust graph fusion through a differentiable neural network. The effectiveness of the proposed method is verified by rigorous comparisons with considerable state-of-the-art approaches in terms of multi-view semi-supervised classification tasks, and the experimental results indicate that MGCN-DNS achieves pleasurable performance on several benchmark multi-view datasets.\n          <\/jats:p>","DOI":"10.1145\/3608954","type":"journal-article","created":{"date-parts":[[2023,7,11]],"date-time":"2023-07-11T11:52:36Z","timestamp":1689076356000},"page":"1-21","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":15,"title":["Multi-View Graph Convolutional Networks with Differentiable Node Selection"],"prefix":"10.1145","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7832-908X","authenticated-orcid":false,"given":"Zhaoliang","family":"Chen","sequence":"first","affiliation":[{"name":"College of Computer and Data Science, Fujian Provincial Key Laboratory ofNetwork Computing and Intelligent Information Processing, Fuzhou University, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5304-0434","authenticated-orcid":false,"given":"Lele","family":"Fu","sequence":"additional","affiliation":[{"name":"School of Systems Science and Engineering, Sun Yat-sen University, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1187-8719","authenticated-orcid":false,"given":"Shunxin","family":"Xiao","sequence":"additional","affiliation":[{"name":"College of Computer and Data Science, Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing, Fuzhou University, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5195-9682","authenticated-orcid":false,"given":"Shiping","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Computer and Data Science, Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing, Fuzhou University, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5274-8123","authenticated-orcid":false,"given":"Claudia","family":"Plant","sequence":"additional","affiliation":[{"name":"Faculty of Computer Science, ds:UniVie, University of Vienna, Austria"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4118-8823","authenticated-orcid":false,"given":"Wenzhong","family":"Guo","sequence":"additional","affiliation":[{"name":"College of Computer and Data Science, Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing, Fuzhou University, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2023,8,10]]},"reference":[{"issue":"3","key":"e_1_3_2_2_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3477051","article-title":"DACHA: A dual graph convolution based temporal knowledge graph representation learning method using historical relation","volume":"16","author":"Chen Ling","year":"2021","unstructured":"Ling Chen, Xing Tang, Weiqi Chen, Yuntao Qian, Yansheng Li, and Yongjun Zhang. 2021. DACHA: A dual graph convolution based temporal knowledge graph representation learning method using historical relation. ACM Transactions on Knowledge Discovery from Data 16, 3 (2021), 1\u201318.","journal-title":"ACM Transactions on Knowledge Discovery from Data"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1145\/3474085.3475699"},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2021.3091435"},{"key":"e_1_3_2_5_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2023.02.013"},{"key":"e_1_3_2_6_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2022.3144017"},{"key":"e_1_3_2_7_2","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2022.3185886"},{"key":"e_1_3_2_8_2","volume-title":"Proceedings of the 7th International Conference on Learning Representations","author":"Grover Aditya","year":"2019","unstructured":"Aditya Grover, Eric Wang, Aaron Zweig, and Stefano Ermon. 2019. Stochastic optimization of sorting networks via continuous relaxations. In Proceedings of the 7th International Conference on Learning Representations."},{"key":"e_1_3_2_9_2","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2020.2986201"},{"key":"e_1_3_2_10_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2021.3139178"},{"key":"e_1_3_2_11_2","first-page":"4116","volume-title":"Proceedings of the 37th International Conference on Machine Learning","volume":"119","author":"Hassani Kaveh","year":"2020","unstructured":"Kaveh Hassani and Amir Hosein Khas Ahmadi. 2020. Contrastive multi-view representation learning on graphs. In Proceedings of the 37th International Conference on Machine Learning, Vol. 119. 4116\u20134126."},{"key":"e_1_3_2_12_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2021.3101917"},{"key":"e_1_3_2_13_2","first-page":"4215","volume-title":"Proceedings of the 34th AAAI Conference on Artificial Intelligence","volume":"34","author":"Hui Binyuan","year":"2020","unstructured":"Binyuan Hui, Pengfei Zhu, and Qinghua Hu. 2020. Collaborative graph convolutional networks: Unsupervised learning meets semi-supervised learning. In Proceedings of the 34th AAAI Conference on Artificial Intelligence, Vol. 34. 4215\u20134222."},{"key":"e_1_3_2_14_2","first-page":"1324","volume-title":"Proceedings of the 29th International Joint Conference on Artificial Intelligence","author":"Jia Ziyu","year":"2020","unstructured":"Ziyu Jia, Youfang Lin, Jing Wang, Ronghao Zhou, Xiaojun Ning, Yuanlai He, and Yaoshuai Zhao. 2020. Graphsleepnet: Adaptive spatial-temporal graph convolutional networks for sleep stage classification. In Proceedings of the 29th International Joint Conference on Artificial Intelligence. 1324\u20131330."},{"key":"e_1_3_2_15_2","first-page":"606","volume-title":"Proceedings of the 31st AAAI Conference on Artificial Intelligence","volume":"33","author":"Khan Muhammad Raza","year":"2019","unstructured":"Muhammad Raza Khan and Joshua E. Blumenstock. 2019. Multi-GCN: Graph convolutional networks for multi-view networks, with applications to global poverty. In Proceedings of the 31st AAAI Conference on Artificial Intelligence, Vol. 33. 606\u2013613."},{"key":"e_1_3_2_16_2","volume-title":"Proceedings of the 5th International Conference on Learning Representations","author":"Kipf Thomas N.","year":"2017","unstructured":"Thomas N. Kipf and Max Welling. 2017. Semi-supervised classification with graph convolutional networks. In Proceedings of the 5th International Conference on Learning Representations."},{"issue":"4","key":"e_1_3_2_17_2","first-page":"72:1\u201372:26","article-title":"Attributed network embedding with micro-meso structure","volume":"15","author":"Li Juan-Hui","year":"2021","unstructured":"Juan-Hui Li, Ling Huang, Chang-Dong Wang, Dong Huang, Jian-Huang Lai, and Pei Chen. 2021. Attributed network embedding with micro-meso structure. ACM Transactions on Knowledge Discovery from Data 15, 4 (2021), 72:1\u201372:26.","journal-title":"ACM Transactions on Knowledge Discovery from Data"},{"key":"e_1_3_2_18_2","first-page":"4691","volume-title":"Proceedings of the 34th AAAI Conference on Artificial Intelligence","author":"Li Shu","year":"2020","unstructured":"Shu Li, Wen-Tao Li, and Wei Wang. 2020. Co-GCN for multi-view semi-supervised learning. In Proceedings of the 34th AAAI Conference on Artificial Intelligence. 4691\u20134698."},{"key":"e_1_3_2_19_2","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2020.2974323"},{"key":"e_1_3_2_20_2","article-title":"DGIG-Net: Dynamic graph-in-graph networks for few-shot human-object interaction","author":"Liu Xiyao","year":"2021","unstructured":"Xiyao Liu, Zhong Ji, Yanwei Pang, Jungong Han, and Xuelong Li. 2021. DGIG-Net: Dynamic graph-in-graph networks for few-shot human-object interaction. IEEE Transactions on Cybernetics 52, 8 (2021), 7852\u20137864.","journal-title":"IEEE Transactions on Cybernetics"},{"issue":"8","key":"e_1_3_2_21_2","first-page":"2634","article-title":"Efficient and effective regularized incomplete multi-view clustering","volume":"43","author":"Liu Xinwang","year":"2021","unstructured":"Xinwang Liu, Miaomiao Li, Chang Tang, Jingyuan Xia, Jian Xiong, Li Liu, Marius Kloft, and En Zhu. 2021. Efficient and effective regularized incomplete multi-view clustering. IEEE Transactions on Pattern Analysis and Machine Intelligence 43, 8 (2021), 2634\u20132646.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"e_1_3_2_22_2","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2022.3224978"},{"key":"e_1_3_2_23_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v31i1.10909"},{"key":"e_1_3_2_24_2","first-page":"1881","volume-title":"Proceedings of the 25th International Joint Conference on Artificial Intelligence","author":"Nie Feiping","year":"2016","unstructured":"Feiping Nie, Jing Li, and Xuelong Li. 2016. Parameter-free auto-weighted multiple graph learning: A framework for multiview clustering and semi-supervised classification. In Proceedings of the 25th International Joint Conference on Artificial Intelligence. 1881\u20131887."},{"key":"e_1_3_2_25_2","doi-asserted-by":"publisher","DOI":"10.5555\/3172077.3172245"},{"key":"e_1_3_2_26_2","doi-asserted-by":"publisher","DOI":"10.1145\/3447548.3467381"},{"key":"e_1_3_2_27_2","first-page":"5892","volume-title":"Proceedings of the 34th AAAI Conference on Artificial Intelligence","author":"Sun Ke","year":"2020","unstructured":"Ke Sun, Zhouchen Lin, and Zhanxing Zhu. 2020. Multi-stage self-supervised learning for graph convolutional networks on graphs with few labeled nodes. In Proceedings of the 34th AAAI Conference on Artificial Intelligence. 5892\u20135899."},{"key":"e_1_3_2_28_2","first-page":"3038","volume-title":"Proceedings of the 30th International Joint Conference on Artificial Intelligence","author":"Tang Chang","year":"2021","unstructured":"Chang Tang, Xinwang Liu, En Zhu, Lizhe Wang, and Albert Y. Zomaya. 2021. Hyperspectral band selection via spatial-spectral weighted region-wise multiple graph fusion-based spectral clustering. In Proceedings of the 30th International Joint Conference on Artificial Intelligence. 3038\u20133044."},{"key":"e_1_3_2_29_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2017.2717191"},{"issue":"9","key":"e_1_3_2_30_2","first-page":"5042","article-title":"Learning deep sparse regularizers with applications to multi-view clustering and semi-supervised classification","volume":"44","author":"Wang Shiping","year":"2022","unstructured":"Shiping Wang, Zhaoliang Chen, Shide Du, and Zhouchen Lin. 2022. Learning deep sparse regularizers with applications to multi-view clustering and semi-supervised classification. IEEE Transactions on Pattern Analysis and Machine Intelligence 44, 9 (2022), 5042\u20135055.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"e_1_3_2_31_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2021.3131941"},{"key":"e_1_3_2_32_2","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403177"},{"key":"e_1_3_2_33_2","doi-asserted-by":"publisher","DOI":"10.1145\/3532191"},{"key":"e_1_3_2_34_2","doi-asserted-by":"publisher","DOI":"10.1145\/3554981"},{"key":"e_1_3_2_35_2","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403118"},{"key":"e_1_3_2_36_2","doi-asserted-by":"publisher","DOI":"10.1145\/3451394"},{"key":"e_1_3_2_37_2","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2022.3187976"},{"key":"e_1_3_2_38_2","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2022.3213208"},{"key":"e_1_3_2_39_2","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2021.3094296"},{"key":"e_1_3_2_40_2","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2023.3248173"},{"key":"e_1_3_2_41_2","doi-asserted-by":"publisher","DOI":"10.1145\/3394171.3414012"},{"key":"e_1_3_2_42_2","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2018.2869789"},{"key":"e_1_3_2_43_2","doi-asserted-by":"publisher","DOI":"10.1145\/3503161.3548144"},{"key":"e_1_3_2_44_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2021.3129117"},{"key":"e_1_3_2_45_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2018.11.015"},{"key":"e_1_3_2_46_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.artint.2022.103708"},{"key":"e_1_3_2_47_2","doi-asserted-by":"publisher","DOI":"10.1145\/3394171.3413941"},{"key":"e_1_3_2_48_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2021.3062692"},{"key":"e_1_3_2_49_2","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2020.2978844"},{"key":"e_1_3_2_50_2","first-page":"7731","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"Zhang Yaobin","year":"2020","unstructured":"Yaobin Zhang, Weihong Deng, Mei Wang, Jiani Hu, Xian Li, Dongyue Zhao, and Dongchao Wen. 2020. Global-local GCN: Large-scale label noise cleansing for face recognition. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 7731\u20137740."},{"key":"e_1_3_2_51_2","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2021.3116936"},{"key":"e_1_3_2_52_2","volume-title":"Proceedings of the 9th International Conference on Learning Representations","author":"Zhu Hao","year":"2021","unstructured":"Hao Zhu and Piotr Koniusz. 2021. Simple spectral graph convolution. In Proceedings of the 9th International Conference on Learning Representations."}],"container-title":["ACM Transactions on Knowledge Discovery from Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3608954","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3608954","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T22:29:46Z","timestamp":1750285786000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3608954"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,8,10]]},"references-count":51,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2024,1,31]]}},"alternative-id":["10.1145\/3608954"],"URL":"https:\/\/doi.org\/10.1145\/3608954","relation":{},"ISSN":["1556-4681","1556-472X"],"issn-type":[{"value":"1556-4681","type":"print"},{"value":"1556-472X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,8,10]]},"assertion":[{"value":"2022-12-05","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2023-07-05","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2023-08-10","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}