{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,23]],"date-time":"2025-10-23T05:43:32Z","timestamp":1761198212731},"reference-count":53,"publisher":"Springer Science and Business Media LLC","issue":"10","license":[{"start":{"date-parts":[[2023,5,15]],"date-time":"2023-05-15T00:00:00Z","timestamp":1684108800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,5,15]],"date-time":"2023-05-15T00:00:00Z","timestamp":1684108800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Knowl Inf Syst"],"published-print":{"date-parts":[[2023,10]]},"DOI":"10.1007\/s10115-023-01886-7","type":"journal-article","created":{"date-parts":[[2023,5,15]],"date-time":"2023-05-15T17:02:53Z","timestamp":1684170173000},"page":"4021-4054","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["GM2NAS: multitask multiview graph neural architecture search"],"prefix":"10.1007","volume":"65","author":[{"given":"Jianliang","family":"Gao","sequence":"first","affiliation":[]},{"given":"Raeed","family":"Al-Sabri","sequence":"additional","affiliation":[]},{"given":"Babatounde Moctard","family":"Oloulade","sequence":"additional","affiliation":[]},{"given":"Jiamin","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Tengfei","family":"Lyu","sequence":"additional","affiliation":[]},{"given":"Zhenpeng","family":"Wu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,5,15]]},"reference":[{"key":"1886_CR1","doi-asserted-by":"crossref","unstructured":"Cai L, Ji S (2020) A multi-scale approach for graph link prediction. In: Proceedings of the conference on artificial intelligence, pp 3308\u20133315","DOI":"10.1609\/aaai.v34i04.5731"},{"key":"1886_CR2","doi-asserted-by":"crossref","unstructured":"Wang X, Zhu M, Bo D, Cui P, Shi C, Pei J (2020) AM-GCN: adaptive multi-channel graph convolutional networks. In: Proceedings of the ACM conference on knowledge discovery and data mining, virtual event, pp 1243\u20131253","DOI":"10.1145\/3394486.3403177"},{"issue":"1","key":"1886_CR3","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1109\/TNNLS.2020.2978386","volume":"32","author":"Z Wu","year":"2021","unstructured":"Wu Z, Pan S, Chen F, Long G, Zhang C, Yu PS (2021) A comprehensive survey on graph neural networks. IEEE Trans Neural Netw Learn Syst 32(1):4\u201324","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"issue":"3","key":"1886_CR4","doi-asserted-by":"publisher","first-page":"983","DOI":"10.1109\/TNNLS.2020.3036825","volume":"33","author":"H Huang","year":"2022","unstructured":"Huang H, Song Y, Wu Y, Shi J, Xie X, Jin H (2022) Multitask representation learning with multiview graph convolutional networks. IEEE Trans Neural Netw Learn Syst 33(3):983\u2013995","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"1886_CR5","unstructured":"Hassani K, Ahmadi AHK (2020) Contrastive multi-view representation learning on graphs. In: Proceedings of the international conference on machine learning, pp 4116\u20134126"},{"key":"1886_CR6","doi-asserted-by":"crossref","unstructured":"Fan S, Wang X, Shi C, Lu E, Lin K, Wang B (2020) One2multi graph autoencoder for multi-view graph clustering. In: Proceedings of the web conference, pp 3070\u20133076","DOI":"10.1145\/3366423.3380079"},{"key":"1886_CR7","doi-asserted-by":"crossref","unstructured":"Chen Z, Zhang X, Cheng X (2022) ASM2TV: an adaptive semi-supervised multi-task multi-view learning framework for human activity recognition. In: Proceedings of the AAAI conference on artificial intelligence, pp 6342\u20136349","DOI":"10.1609\/aaai.v36i6.20584"},{"key":"1886_CR8","doi-asserted-by":"crossref","unstructured":"Zhang Z, Wang X, Zhu W (2021) Automated machine learning on graphs: A survey. In: Proceedings of the international joint conference on artificial intelligence, pp 4704\u20134712","DOI":"10.24963\/ijcai.2021\/637"},{"issue":"4","key":"1886_CR9","doi-asserted-by":"publisher","first-page":"692","DOI":"10.26599\/TST.2021.9010057","volume":"27","author":"BM Oloulade","year":"2022","unstructured":"Oloulade BM, Gao J, Chen J, Lyu T, Al-Sabri R (2022) Graph neural architecture search: a survey. Tsinghua Sci Technol 27(4):692\u2013708","journal-title":"Tsinghua Sci Technol"},{"issue":"3","key":"1886_CR10","doi-asserted-by":"publisher","first-page":"933","DOI":"10.1007\/s10115-016-0926-z","volume":"49","author":"S Gupta","year":"2016","unstructured":"Gupta S, Rana S, Saha B, Phung D, Venkatesh S (2016) A new transfer learning framework with application to model-agnostic multi-task learning. Knowl Inf Syst 49(3):933\u2013973","journal-title":"Knowl Inf Syst"},{"issue":"8","key":"1886_CR11","first-page":"27503","volume":"34","author":"C Fifty","year":"2021","unstructured":"Fifty C, Amid E, Zhao Z, Yu T, Anil R, Finn C (2021) Efficiently identifying task groupings for multi-task learning. Adv Neural Inf Process Syst 34(8):27503\u201327516","journal-title":"Adv Neural Inf Process Syst"},{"issue":"7","key":"1886_CR12","doi-asserted-by":"publisher","first-page":"3952","DOI":"10.1109\/TII.2018.2884211","volume":"15","author":"C Hong","year":"2018","unstructured":"Hong C, Yu J, Zhang J, Jin X, Lee K-H (2018) Multimodal face-pose estimation with multitask manifold deep learning. IEEE Trans Ind Inf 15(7):3952\u20133961","journal-title":"IEEE Trans Ind Inf"},{"issue":"1","key":"1886_CR13","first-page":"1","volume":"1","author":"J Zhang","year":"2021","unstructured":"Zhang J, Su Q, Tang B, Wang C, Li Y (2021) Dpsnet: multitask learning using geometry reasoning for scene depth and semantics. IEEE Trans Neural Netw Learn Syst 1(1):1\u201312","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"issue":"12","key":"1886_CR14","doi-asserted-by":"publisher","first-page":"5659","DOI":"10.1109\/TIP.2015.2487860","volume":"24","author":"C Hong","year":"2015","unstructured":"Hong C, Yu J, Wan J, Tao D, Wang M (2015) Multimodal deep autoencoder for human pose recovery. IEEE Trans Image Process 24(12):5659\u20135670","journal-title":"IEEE Trans Image Process"},{"issue":"6","key":"1886_CR15","first-page":"3742","volume":"62","author":"C Hong","year":"2014","unstructured":"Hong C, Yu J, Tao D, Wang M (2014) Image-based three-dimensional human pose recovery by multiview locality-sensitive sparse retrieval. IEEE Trans Ind Electronics 62(6):3742\u20133751","journal-title":"IEEE Trans Ind Electronics"},{"issue":"8","key":"1886_CR16","doi-asserted-by":"publisher","first-page":"2048","DOI":"10.1109\/TMM.2019.2947358","volume":"22","author":"X Lu","year":"2019","unstructured":"Lu X, Zhu L, Li J, Zhang H, Shen HT (2019) Efficient supervised discrete multi-view hashing for large-scale multimedia search. IEEE Trans Multimedia 22(8):2048\u20132060","journal-title":"IEEE Trans Multimedia"},{"key":"1886_CR17","doi-asserted-by":"crossref","unstructured":"Lu X, Zhu L, Cheng Z, Nie L, Zhang H (2019) Online multi-modal hashing with dynamic query-adaption. In: Proceedings of the international ACM SIGIR conference on research and development in information retrieval, pp 715\u2013724","DOI":"10.1145\/3331184.3331217"},{"key":"1886_CR18","doi-asserted-by":"crossref","unstructured":"Lu X, Zhu L, Cheng Z, Li J, Nie X, Zhang H (2019) Flexible online multi-modal hashing for large-scale multimedia retrieval. In: Proceedings of the ACM international conference on multimedia, pp 1129\u20131137","DOI":"10.1145\/3343031.3350999"},{"issue":"1","key":"1886_CR19","first-page":"182","volume":"403","author":"J Zhang","year":"2020","unstructured":"Zhang J, Su Q, Wang C, Gu H (2020) Monocular 3d vehicle detection with multi-instance depth and geometry reasoning for autonomous driving. Neurocomputing 403(1):182\u2013192","journal-title":"Neurocomputing"},{"key":"1886_CR20","doi-asserted-by":"crossref","unstructured":"Vandenhende S, Georgoulis S, Gool LV (2020) Mti-net: Multi-scale task interaction networks for multi-task learning. In: European conference on computer vision, pp 527\u2013543","DOI":"10.1007\/978-3-030-58548-8_31"},{"issue":"1","key":"1886_CR21","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s00138-021-01251-0","volume":"33","author":"S Xiao","year":"2022","unstructured":"Xiao S, Wang S, Dai Y, Guo W (2022) Graph neural networks in node classification: survey and evaluation. Machine Vis Appl 33(1):1\u201319","journal-title":"Machine Vis Appl"},{"key":"1886_CR22","unstructured":"Tran PV (2018) Multi-task graph autoencoders. In Proceedings of the NIPS workshop on relational representation learning, pp 1\u20139"},{"key":"1886_CR23","doi-asserted-by":"crossref","unstructured":"Ma Y, Ren Z, Jiang Z, Tang J, Yin D (2018) Multi-dimensional network embedding with hierarchical structure. In: Proceedings of the ACM international conference on web search and data mining, pp 387\u2013395","DOI":"10.1145\/3159652.3159680"},{"key":"1886_CR24","doi-asserted-by":"crossref","unstructured":"Qu M, Tang J, Shang J, Ren X, Zhang M, Han J (2017) An attention-based collaboration framework for multi-view network representation learning. In: Proceedings of the ACM on conference on information and knowledge management, pp 1767\u20131776","DOI":"10.1145\/3132847.3133021"},{"issue":"1","key":"1886_CR25","doi-asserted-by":"publisher","first-page":"29","DOI":"10.1007\/s10115-015-0872-1","volume":"48","author":"J Wu","year":"2016","unstructured":"Wu J, Hong Z, Pan S, Zhu X, Cai Z, Zhang C (2016) Multi-graph-view subgraph mining for graph classification. Knowl Inf Syst 48(1):29\u201354","journal-title":"Knowl Inf Syst"},{"key":"1886_CR26","doi-asserted-by":"crossref","unstructured":"Lu C, He L, Shao W, Cao B, Yu PS (2017) Multilinear factorization machines for multi-task multi-view learning. In: Proceedings of the ACM international conference on web search and data mining, pp 701\u2013709","DOI":"10.1145\/3018661.3018716"},{"key":"1886_CR27","doi-asserted-by":"crossref","unstructured":"Wang M, Lin Y, Lin G, Yang K, Wu X-m (2020) M2grl: A multi-task multi-view graph representation learning framework for web-scale recommender systems. In: Proceedings of the ACM SIGKDD international conference on knowledge discovery and data mining, pp 2349\u20132358","DOI":"10.1145\/3394486.3403284"},{"key":"1886_CR28","doi-asserted-by":"crossref","unstructured":"Li Y, King I (2020) Autograph: Automated graph neural network. In: Proceedings of the international conference on neural information processing, pp 189\u2013201","DOI":"10.1007\/978-3-030-63833-7_16"},{"key":"1886_CR29","unstructured":"Zhao H, Wei L, Yao Q (2020) Simplifying architecture search for graph neural network. In: Proceedings of the ACM international conference on information and knowledge management, pp 1\u201312"},{"key":"1886_CR30","doi-asserted-by":"crossref","unstructured":"Chen J, Gao J, Chen Y, Oloulade MB, Lyu T, Li Z (2021) Graphpas: Parallel architecture search for graph neural networks. In: Proceedings of the international ACM conference on research and development in information retrieval, pp 2182\u20132186","DOI":"10.1145\/3404835.3463007"},{"key":"1886_CR31","doi-asserted-by":"crossref","unstructured":"Yoon M, Gervet T, Hooi B, Faloutsos C (2020) Autonomous graph mining algorithm search with best speed\/accuracy trade-off. In: Proceedings of the IEEE international conference on data mining, pp 751\u2013760","DOI":"10.1109\/ICDM50108.2020.00084"},{"issue":"5","key":"1886_CR32","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3465055","volume":"12","author":"S Chaudhari","year":"2021","unstructured":"Chaudhari S, Mithal V, Polatkan G, Ramanath R (2021) An attentive survey of attention models. ACM Trans Intell Syst Technol 12(5):1\u201332","journal-title":"ACM Trans Intell Syst Technol"},{"issue":"1","key":"1886_CR33","doi-asserted-by":"publisher","first-page":"106","DOI":"10.1016\/j.neucom.2021.03.090","volume":"448","author":"X Yan","year":"2021","unstructured":"Yan X, Hu S, Mao Y, Ye Y, Yu H (2021) Deep multi-view learning methods: a review. Neurocomputing 448(1):106\u2013129","journal-title":"Neurocomputing"},{"issue":"10","key":"1886_CR34","doi-asserted-by":"publisher","first-page":"1863","DOI":"10.1109\/TKDE.2018.2872063","volume":"31","author":"Y Li","year":"2019","unstructured":"Li Y, Yang M, Zhang Z (2019) A survey of multi-view representation learning. IEEE Trans Knowl Data Eng 31(10):1863\u20131883","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"1886_CR35","unstructured":"Lin B, Ye F, Zhang Y (2021) A closer look at loss weighting in multi-task learning. arXiv preprint arXiv:2111.10603"},{"issue":"1","key":"1886_CR36","first-page":"108","volume":"247","author":"M Shi","year":"2022","unstructured":"Shi M, Tang Y, Zhu X, Huang Y, Wilson DA, Zhuang Y, Liu J (2022) Genetic-gnn: Evolutionary architecture search for graph neural networks. Knowl Based Syst 247(1):108\u2013128","journal-title":"Knowl Based Syst"},{"key":"1886_CR37","unstructured":"You J, Ying Z, Leskovec J (2020) Design space for graph neural networks 10(1):1\u201312"},{"key":"1886_CR38","doi-asserted-by":"crossref","unstructured":"Li Q, Han Z, Wu X (2018) Deeper insights into graph convolutional networks for semi-supervised learning. In: Proceedings of the AAAI conference on artificial intelligence, pp 3538\u20133545","DOI":"10.1609\/aaai.v32i1.11604"},{"key":"1886_CR39","doi-asserted-by":"crossref","unstructured":"Tang L, Liu H (2009) Relational learning via latent social dimensions. In: Proceedings of the ACM international conference on knowledge discovery and data mining, pp 817\u2013826","DOI":"10.1145\/1557019.1557109"},{"key":"1886_CR40","unstructured":"Ramos J (2003) Using tf-idf to determine word relevance in document queries. In: Proceedings of the instructional conference on machine learning, pp 29\u201348"},{"key":"1886_CR41","doi-asserted-by":"crossref","unstructured":"Dong Y, Chawla NV, Swami A (2017) metapath2vec: Scalable representation learning for heterogeneous networks. In: Proceedings of the ACM international conference on knowledge discovery and data mining, pp 135\u2013144","DOI":"10.1145\/3097983.3098036"},{"issue":"1","key":"1886_CR42","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/srep02980","volume":"3","author":"M De Domenico","year":"2013","unstructured":"De Domenico M, Lima A, Mougel P, Musolesi M (2013) The anatomy of a scientific rumor. Sci Rep 3(1):1\u20139","journal-title":"Sci Rep"},{"key":"1886_CR43","doi-asserted-by":"crossref","unstructured":"Tang J, Qu M, Wang M, Zhang M, Yan J, Mei Q (2015) LINE: large-scale information network embedding. In: Proceedings of the international conference on world wide web, pp 1067\u20131077","DOI":"10.1145\/2736277.2741093"},{"key":"1886_CR44","unstructured":"Zafarani R, Liu H (2009) Social computing data repository at asu \\url{http:\/\/socialcomputing.asu.edu}. Arizona State University, School of Computing, Informatics and Decision Systems Engineering"},{"key":"1886_CR45","doi-asserted-by":"crossref","unstructured":"Perozzi B, Al-Rfou R, Skiena S (2014) Deepwalk: Online learning of social representations. In: Proceedings of the ACM international conference on knowledge discovery and data mining, pp 701\u2013710","DOI":"10.1145\/2623330.2623732"},{"key":"1886_CR46","unstructured":"Kipf TN, Welling M (2017) Semi-supervised classification with graph convolutional networks. In: Proceddings of the international conference on learning representations, pp 1\u201312"},{"key":"1886_CR47","unstructured":"Velickovic P, Cucurull G, Casanova A, Romero A, Li\u00f2 P, Bengio Y (2015) Graph attention networks. In: Proceedings of the international conference on learning representations, pp 1\u201320"},{"issue":"1","key":"1886_CR48","first-page":"1024","volume":"30","author":"W Hamilton","year":"2017","unstructured":"Hamilton W, Ying Z, Leskovec J (2017) Inductive representation learning on large graphs. Adv Neural Inf Process Syst 30(1):1024\u20131034","journal-title":"Adv Neural Inf Process Syst"},{"key":"1886_CR49","doi-asserted-by":"crossref","unstructured":"Schlichtkrull MS, Kipf TN, Bloem P, van\u00a0den Berg R, Titov I, Welling M (2018) Modeling relational data with graph convolutional networks. In: Proceedings of the semantic web international conference, pp 593\u2013607","DOI":"10.1007\/978-3-319-93417-4_38"},{"key":"1886_CR50","doi-asserted-by":"crossref","unstructured":"Fu T, Lee W, Lei Z (2017) Hin2vec: Explore meta-paths in heterogeneous information networks for representation learning. In: Proceedings of the ACM conference on information and knowledge management, pp 1797\u20131806","DOI":"10.1145\/3132847.3132953"},{"issue":"1","key":"1886_CR51","doi-asserted-by":"publisher","first-page":"180","DOI":"10.1016\/j.neunet.2020.08.021","volume":"132","author":"Y Xie","year":"2020","unstructured":"Xie Y, Zhang Y, Gong M, Tang Z, Han C (2020) Mgat: Multi-view graph attention networks. Neural Netw 132(1):180\u2013189","journal-title":"Neural Netw"},{"issue":"1","key":"1886_CR52","first-page":"8728","volume":"33","author":"X Sun","year":"2020","unstructured":"Sun X, Panda R, Feris R, Saenko K (2020) Adashare: learning what to share for efficient deep multi-task learning. Adv Neural Inf Process Syst 33(1):8728\u20138740","journal-title":"Adv Neural Inf Process Syst"},{"key":"1886_CR53","doi-asserted-by":"crossref","unstructured":"Li Z, Liu F, Yang W, Peng S, Zhou J (2021) A survey of convolutional neural networks: analysis, applications, and prospects. IEEE Trans Neural Netw Learn Syst","DOI":"10.1109\/TNNLS.2020.3007412"}],"container-title":["Knowledge and Information Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10115-023-01886-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10115-023-01886-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10115-023-01886-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,8,22]],"date-time":"2023-08-22T17:08:50Z","timestamp":1692724130000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10115-023-01886-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,5,15]]},"references-count":53,"journal-issue":{"issue":"10","published-print":{"date-parts":[[2023,10]]}},"alternative-id":["1886"],"URL":"https:\/\/doi.org\/10.1007\/s10115-023-01886-7","relation":{},"ISSN":["0219-1377","0219-3116"],"issn-type":[{"value":"0219-1377","type":"print"},{"value":"0219-3116","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,5,15]]},"assertion":[{"value":"2 December 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 March 2023","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 April 2023","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 May 2023","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"No conflicts of interest or competing interests","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"Not applicable","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval"}},{"value":"The manuscript is approved by all authors for publication.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to participate"}},{"value":"All authors who participated in this study give the publisher permission to publish this work.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"Please email alsabriraeed@csu.edu.cn to request code for the proposed method.","order":6,"name":"Ethics","group":{"name":"EthicsHeading","label":"Code availability"}}]}}