{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T15:27:01Z","timestamp":1772119621735,"version":"3.50.1"},"reference-count":55,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,8,16]],"date-time":"2024-08-16T00:00:00Z","timestamp":1723766400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2024,8,16]],"date-time":"2024-08-16T00:00:00Z","timestamp":1723766400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"DOI":"10.13039\/501100001809","name":"The National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["52174141"],"award-info":[{"award-number":["52174141"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"The Natural Science Foundation of Anhui Province","award":["2108085ME158"],"award-info":[{"award-number":["2108085ME158"]}]},{"name":"The Suzhou Key Research and Development Project","award":["SGC2021069"],"award-info":[{"award-number":["SGC2021069"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Discov Computing"],"DOI":"10.1007\/s10791-024-09461-6","type":"journal-article","created":{"date-parts":[[2024,8,16]],"date-time":"2024-08-16T04:02:44Z","timestamp":1723780964000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A graph residual generation network for node classification based on multi-information aggregation"],"prefix":"10.1007","volume":"27","author":[{"given":"Zhenhuan","family":"Liang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaofen","family":"Jia","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaolei","family":"Han","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Baiting","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhu","family":"Feng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,8,16]]},"reference":[{"key":"9461_CR1","doi-asserted-by":"publisher","unstructured":"Kipf TN, Welling M. Semi-supervised classification with graph convolutional networks. arXiv Preprint. 2016; arXiv: 1609.02907. https:\/\/doi.org\/10.48550\/arXiv.1609.02907","DOI":"10.48550\/arXiv.1609.02907"},{"key":"9461_CR2","doi-asserted-by":"publisher","unstructured":"Veli\u010dkovi\u0107 P, Cucurull G, Casanova A, et al. Graph attention networks. International Conference on Learning Representation. Vancouver: ICLR. 2018; pp 1\u201312. https:\/\/doi.org\/10.48550\/arXiv.1710.10903","DOI":"10.48550\/arXiv.1710.10903"},{"key":"9461_CR3","doi-asserted-by":"publisher","unstructured":"Hamilton WL, Ying R, Leskovec J. Inductive representation learning on large graphs. arXiv Preprint. 2017; arXiv: 1706.02216. https:\/\/doi.org\/10.48550\/arXiv.1706.02216","DOI":"10.48550\/arXiv.1706.02216"},{"issue":"12","key":"9461_CR4","doi-asserted-by":"publisher","first-page":"2285","DOI":"10.3390\/rs13122285","volume":"13","author":"C Zhang","year":"2021","unstructured":"Zhang C, Wang J, Yao K. Global random graph convolution network for hyperspectral image classification. Remote Sens. 2021;13(12):2285. https:\/\/doi.org\/10.3390\/rs13122285.","journal-title":"Remote Sens"},{"key":"9461_CR5","doi-asserted-by":"publisher","unstructured":"Chen D, Lin Y, Li W, et al. Measuring and relieving the over-smoothing problem for graph neural networks from the topological view. In Proceedings of the AAAI Conference on Artificial Intelligence. New York: AAAI. 2020;34(04):3438\u20133445. https:\/\/doi.org\/10.48550\/arXiv.1909.03211","DOI":"10.48550\/arXiv.1909.03211"},{"issue":"12","key":"9461_CR6","doi-asserted-by":"publisher","first-page":"1795","DOI":"10.1631\/FITEE.1900663","volume":"21","author":"H Wang","year":"2020","unstructured":"Wang H, Dong L, Fan T, et al. A local density optimization method based on a graph convolutional network. Front Inform Technol Electron Eng. 2020;21(12):1795\u2013803. https:\/\/doi.org\/10.1631\/FITEE.1900663.","journal-title":"Front Inform Technol Electron Eng"},{"issue":"1","key":"9461_CR7","doi-asserted-by":"publisher","first-page":"2313","DOI":"10.1080\/09540091.2022.2115010","volume":"34","author":"J Qin","year":"2022","unstructured":"Qin J, Zeng X, Wu S, et al. Multi-semantic alignment graph convolutional network. Connect Sci. 2022;34(1):2313\u201331. https:\/\/doi.org\/10.1080\/09540091.2022.2115010.","journal-title":"Connect Sci"},{"key":"9461_CR8","doi-asserted-by":"publisher","unstructured":"Rong Y, Huang W, Xu T, et al. Dropedge: towards deep graph convolutional networks on node classification. Proceedings of Workshop at ICLR. Scottsdale: ICLR. 2020. https:\/\/doi.org\/10.48550\/arXiv.1907.10903","DOI":"10.48550\/arXiv.1907.10903"},{"key":"9461_CR9","doi-asserted-by":"publisher","unstructured":"Liao J, Liu F, Zheng J. A dynamic adaptive multi-view fusion graph convolutional network recommendation model with dilated mask convolution mechanism. Inform Sci. 2024; 658. https:\/\/doi.org\/10.1016\/j.ins.2023.120028","DOI":"10.1016\/j.ins.2023.120028"},{"key":"9461_CR10","doi-asserted-by":"publisher","first-page":"1290491","DOI":"10.3389\/frai.2024.1290491","volume":"7","author":"Z Ye","year":"2024","unstructured":"Ye Z, Li Z, Li G, et al. Dual-channel deep graph convolutional neural networks. Front Artif Intell. 2024;7:1290491. https:\/\/doi.org\/10.3389\/frai.2024.1290491.","journal-title":"Front Artif Intell"},{"key":"9461_CR11","doi-asserted-by":"publisher","unstructured":"Meng L, Ye Z, Yang Y, et al. DeepMCGCN: Multi-channel deep graph neural networks. Int J Comput Intell Syst. 2024;17(1). https:\/\/doi.org\/10.1007\/s44196-024-00432-9","DOI":"10.1007\/s44196-024-00432-9"},{"key":"9461_CR12","doi-asserted-by":"publisher","unstructured":"Tan S, Li D, Jiang R, et al. Community-invariant graph contrastive learning. In Proceedings of the 33rd International conference on Machine learning. Vienna: ICML. 2024. https:\/\/doi.org\/10.48550\/arXiv.2405.01350","DOI":"10.48550\/arXiv.2405.01350"},{"key":"9461_CR13","doi-asserted-by":"publisher","unstructured":"Li D, Wang Y, Funakoshi K, et al. Joyful: joint modality fusion and graph contrastive learning for multimodal emotion recognition. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing. Singapore: EMNLP. 2023. https:\/\/doi.org\/10.48550\/arXiv.2311.11009","DOI":"10.48550\/arXiv.2311.11009"},{"key":"9461_CR14","doi-asserted-by":"publisher","unstructured":"Li D, Tan S, Wang Y, et al. Temporal and topological augmentation-based cross-view contrastive learning model for temporal link prediction. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. Shanghai: CIKM. 2023; 4059\u20134063. https:\/\/doi.org\/10.1145\/3583780.3615231","DOI":"10.1145\/3583780.3615231"},{"issue":"1","key":"9461_CR15","doi-asserted-by":"publisher","first-page":"132","DOI":"10.1214\/18-AOAS1205","volume":"13","author":"T Li","year":"2019","unstructured":"Li T, Levina E, Zhu J. Prediction models for network-linked data. Ann Appl Stat. 2019;13(1):132\u201364. https:\/\/doi.org\/10.1214\/18-AOAS1205.","journal-title":"Ann Appl Stat"},{"key":"9461_CR16","doi-asserted-by":"publisher","unstructured":"Ma J, Tang W, Zhu J, et al. A flexible generative framework for graph-based semi-supervised learning. Advances in Neural Information Processing Systems. Vancouver: NIPS. 2019; pp 3276\u20133285. https:\/\/doi.org\/10.48550\/arXiv.1905.10769","DOI":"10.48550\/arXiv.1905.10769"},{"key":"9461_CR17","doi-asserted-by":"publisher","unstructured":"Baradaaji A, Dornaika F. Joint latent space and label inference estimation with adaptive fused data and label graphs. ACM Trans Intell Syst Technol. 2023;14(4). https:\/\/doi.org\/10.1145\/3590172","DOI":"10.1145\/3590172"},{"issue":"3","key":"9461_CR18","doi-asserted-by":"publisher","first-page":"904","DOI":"10.1007\/s12559-023-10123-w","volume":"15","author":"N Ziraki","year":"2023","unstructured":"Ziraki N, Bosaghzadeh A, Dornaika F, et al. Inductive multi-view semi-supervised learning with a consensus graph. Cogn Comput. 2023;15(3):904\u201313. https:\/\/doi.org\/10.1007\/s12559-023-10123-w.","journal-title":"Cogn Comput"},{"issue":"2","key":"9461_CR19","doi-asserted-by":"publisher","first-page":"1835","DOI":"10.1109\/TPAMI.2022.3166894","volume":"45","author":"Y Zhang","year":"2023","unstructured":"Zhang Y, Ji S, Zou C, et al. Graph learning on millions of data in seconds: label propagation acceleration on graph using data distribution. IEEE Trans Pattern Anal Mach Intell. 2023;45(2):1835\u201347. https:\/\/doi.org\/10.1109\/TPAMI.2022.3166894.","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"9461_CR20","doi-asserted-by":"publisher","unstructured":"Chen Z, Fu L, Xiao S, et al. Multi-view graph convolutional networks with differentiable node selection. ACM Trans Knowl Discov Data. 2024;18(1). https:\/\/doi.org\/10.1145\/3608954","DOI":"10.1145\/3608954"},{"issue":"2","key":"9461_CR21","doi-asserted-by":"publisher","first-page":"667","DOI":"10.1109\/TPAMI.2023.3272341","volume":"46","author":"T Wang","year":"2024","unstructured":"Wang T, Dou Z, Bao C, et al. Diffusion mechanism in residual neural network: theory and applications. IEEE Trans Pattern Anal Mach Intell. 2024;46(2):667\u201380. https:\/\/doi.org\/10.1109\/TPAMI.2023.3272341.","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"4","key":"9461_CR22","doi-asserted-by":"publisher","first-page":"1065","DOI":"10.1109\/TETCI.2022.3222545","volume":"7","author":"M Nie","year":"2023","unstructured":"Nie M, Chen D, Wang D. Reinforcement learning on graphs: a survey. IEEE Trans Emerg Topics Comput Intell. 2023;7(4):1065\u201382. https:\/\/doi.org\/10.1109\/TETCI.2022.3222545.","journal-title":"IEEE Trans Emerg Topics Comput Intell"},{"issue":"6","key":"9461_CR23","doi-asserted-by":"publisher","first-page":"5879","DOI":"10.1109\/TKDE.2022.3172903","volume":"35","author":"Y Liu","year":"2023","unstructured":"Liu Y, Jin M, Pan S, et al. Graph self-supervised learning: a survey. IEEE Trans Knowl Data Eng. 2023;35(6):5879\u2013900. https:\/\/doi.org\/10.1109\/TKDE.2022.3172903.","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"9461_CR24","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-023-09328-9","author":"Z Huang","year":"2024","unstructured":"Huang Z, Li F, Yao J, et al. MGCRL: Multi-view graph convolution and multi-agent reinforcement learning for dialogue state tracking. Neural Comput Appl. 2024. https:\/\/doi.org\/10.1007\/s00521-023-09328-9.","journal-title":"Neural Comput Appl"},{"issue":"4","key":"9461_CR25","doi-asserted-by":"publisher","first-page":"3309","DOI":"10.1109\/TSG.2023.3240580","volume":"14","author":"Q Xing","year":"2023","unstructured":"Xing Q, Xu Y, Chen Z. A bilevel graph reinforcement learning method for electric vehicle fleet charging guidance. IEEE Trans Smart Grid. 2023;14(4):3309\u201312. https:\/\/doi.org\/10.1109\/TSG.2023.3240580.","journal-title":"IEEE Trans Smart Grid"},{"issue":"1","key":"9461_CR26","doi-asserted-by":"publisher","first-page":"2588","DOI":"10.1109\/TIV.2023.3297310","volume":"9","author":"D Xu","year":"2024","unstructured":"Xu D, Liu P, Li H, et al. Multi-view graph convolution network reinforcement learning for CAVs cooperative control in highway mixed traffic. IEEE Trans Intell Vehicles. 2024;9(1):2588\u201399. https:\/\/doi.org\/10.1109\/TIV.2023.3297310.","journal-title":"IEEE Trans Intell Vehicles"},{"key":"9461_CR27","doi-asserted-by":"publisher","unstructured":"Song Y, Yang X and Xu C. Self-supervised calorie-aware heterogeneous graph networks for food recommendation. ACM Trans Multimed Comput Commun Appl. 2023;19(1). https:\/\/doi.org\/10.1145\/3524618","DOI":"10.1145\/3524618"},{"key":"9461_CR28","doi-asserted-by":"publisher","unstructured":"Hoang N, Maehara T. Revisiting graph neural networks: all we have is low-pass filters. arXiv Preprint. 2019; arXiv: 1905.19550. https:\/\/doi.org\/10.48550\/arXiv.1905.09550","DOI":"10.48550\/arXiv.1905.09550"},{"key":"9461_CR29","doi-asserted-by":"publisher","unstructured":"Li G, M\u00fcller M, Thabet A, et al. DeepGCNs: Can GCNs Go As Deep As CNNs? In 2019 IEEE\/CVF international conference on computer vision. Seoul: IEEE. 2019; p. 9266\u20139275. https:\/\/doi.org\/10.48550\/arXiv.1904.03751","DOI":"10.48550\/arXiv.1904.03751"},{"issue":"8","key":"9461_CR30","doi-asserted-by":"publisher","first-page":"11449","DOI":"10.1007\/s11063-023-11383-1","volume":"55","author":"F Hu","year":"2023","unstructured":"Hu F, Song P, He R, et al. MSARN: a multi-scale attention residual network for end-to-end environmental sound classification. Neural Process Lett. 2023;55(8):11449\u201365. https:\/\/doi.org\/10.1007\/s11063-023-11383-1.","journal-title":"Neural Process Lett"},{"issue":"3\u20134","key":"9461_CR31","doi-asserted-by":"publisher","first-page":"459","DOI":"10.1177\/10775463231151721","volume":"30","author":"W Du","year":"2024","unstructured":"Du W, Yang L, Wang H, et al. LN-MRSCAE: A novel deep learning based denoising method for mechanical vibration signals. J Vib Control. 2024;30(3\u20134):459\u201371. https:\/\/doi.org\/10.1177\/10775463231151721.","journal-title":"J Vib Control"},{"key":"9461_CR32","doi-asserted-by":"publisher","DOI":"10.1007\/s13042-024-02136-0","author":"S Chen","year":"2024","unstructured":"Chen S, Zhang C, Gu F, et al. RSGNN: residual structure graph neural network. Int J Mach Learn Cybern. 2024. https:\/\/doi.org\/10.1007\/s13042-024-02136-0.","journal-title":"Int J Mach Learn Cybern"},{"key":"9461_CR33","doi-asserted-by":"publisher","unstructured":"He K, Zhang X, Ren S, et al. Deep residual learning for image recognition. In: 2016 IEEE conference on computer vision and pattern recognition. Las Vegas: IEEE. 2016; p. 770\u2013778. https:\/\/doi.org\/10.48550\/arXiv.1512.03385","DOI":"10.48550\/arXiv.1512.03385"},{"key":"9461_CR34","doi-asserted-by":"publisher","unstructured":"Mikolov T, Chen K, Corrado G, et al. Efficient estimation of word representations in vector space. In: Proceedings of Workshop at ICLR. Scottsdale: ICLR. 2013. https:\/\/doi.org\/10.48550\/arXiv.1301.3781","DOI":"10.48550\/arXiv.1301.3781"},{"key":"9461_CR35","doi-asserted-by":"publisher","unstructured":"Tang J, Qu M, Wang M, et al. Line: Large-scale information network embedding. In: Proceedings of the 24th international conference on World Wide Web. Florence: WWW. 2015; p. 1067\u20131077. https:\/\/doi.org\/10.48550\/arXiv.1503.03578","DOI":"10.48550\/arXiv.1503.03578"},{"key":"9461_CR36","doi-asserted-by":"publisher","first-page":"93","DOI":"10.1609\/aimag.v29i3.2157","volume":"29","author":"P Sen","year":"2008","unstructured":"Sen P, Namata G, Bilgic M, et al. Collective classification in network data. AI Mag. 2008;29:93\u2013106. https:\/\/doi.org\/10.1609\/aimag.v29i3.2157.","journal-title":"AI Mag"},{"key":"9461_CR37","doi-asserted-by":"publisher","unstructured":"Yang Z, Cohen W, Salakhutdinov R. Revisiting semi-supervised learning with graph embeddings. In: Proceedings of the 33rd International conference on Machine learning. New York: ICML. 2016; pp 40\u201348. https:\/\/doi.org\/10.48550\/arXiv.1603.08861","DOI":"10.48550\/arXiv.1603.08861"},{"key":"9461_CR38","doi-asserted-by":"publisher","unstructured":"Rozemberczki B, Allen C, Sarkar R. Multi-scale attributed node embedding. J Complex Netw. 2021;9(2). https:\/\/doi.org\/10.48550\/arXiv.1909.13021","DOI":"10.48550\/arXiv.1909.13021"},{"key":"9461_CR39","doi-asserted-by":"publisher","unstructured":"Pei H, Wei B, Yu L, et al. Geom-GCN: geometric graph convolutional networks. arXiv Preprint. 2020; arXiv: 2002.05287. https:\/\/doi.org\/10.48550\/arXiv.2002.05287","DOI":"10.48550\/arXiv.2002.05287"},{"key":"9461_CR40","doi-asserted-by":"publisher","unstructured":"Kingma D, Ba J. Adam: A method for stochastic optimization. arXiv Preprint. 2014; arXiv: 1412.6980. https:\/\/doi.org\/10.48550\/arXiv.1412.6980","DOI":"10.48550\/arXiv.1412.6980"},{"key":"9461_CR41","doi-asserted-by":"publisher","unstructured":"Xu K, Li C, Tian Y, et al. Representation learning on graphs with jumping knowledge networks. In: Proceedings of the 35rd International conference on Machine learning. Stockholm: ICML. 2018; p. 5453\u20135462. https:\/\/doi.org\/10.48550\/arXiv.1806.03536","DOI":"10.48550\/arXiv.1806.03536"},{"key":"9461_CR42","doi-asserted-by":"publisher","unstructured":"Klicpera J; Bojchevski A, G\u00fcnnemann S. Predict then propagate: Graph neural networks meet personalized pagerank. arXiv Preprint. 2018; arXiv: 1810.05997. https:\/\/doi.org\/10.48550\/arXiv.1810.05997","DOI":"10.48550\/arXiv.1810.05997"},{"key":"9461_CR43","doi-asserted-by":"publisher","first-page":"462","DOI":"10.1016\/j.patrec.2020.08.015","volume":"138","author":"Y Liu","year":"2020","unstructured":"Liu Y, Wang Q, Wang X, et al. Community enhanced graph convolutional networks. Pattern Recogn Lett. 2020;138:462\u20138. https:\/\/doi.org\/10.1016\/j.patrec.2020.08.015.","journal-title":"Pattern Recogn Lett"},{"key":"9461_CR44","doi-asserted-by":"publisher","unstructured":"Mahsa M, Abdessamad B. Anisotropic graph convolutional network for semi-supervised learning. IEEE Trans Multimed. 2021;23:3931\u20133942. https:\/\/doi.org\/10.48550\/arXiv.2010.10284","DOI":"10.48550\/arXiv.2010.10284"},{"issue":"10","key":"9461_CR45","doi-asserted-by":"publisher","first-page":"3441","DOI":"10.1007\/s10489-020-01729-w","volume":"50","author":"Y Feng","year":"2020","unstructured":"Feng Y, Li K, Gao Y, et al. Hierarchical graph attention networks for semi-supervised node classification. Appl Intell. 2020;50(10):3441\u201351. https:\/\/doi.org\/10.1007\/s10489-020-01729-w.","journal-title":"Appl Intell"},{"key":"9461_CR46","doi-asserted-by":"publisher","unstructured":"Min Y, Frederik W, Guy W. Geometric scattering attention networks. In: ICASSP 2021\u20132021 IEEE international conference on acoustics, speech and signal processing (ICASSP). 2021; p. 8518\u20138522. https:\/\/doi.org\/10.1109\/ICASSP39728.2021.9414557","DOI":"10.1109\/ICASSP39728.2021.9414557"},{"key":"9461_CR47","doi-asserted-by":"publisher","unstructured":"Negar H, Alexandros I. Progressive graph convolutional networks for semi-supervised node classification. IEEE Access. 2021;9:81957\u201381968. https:\/\/doi.org\/10.48550\/arXiv.2003.12277","DOI":"10.48550\/arXiv.2003.12277"},{"key":"9461_CR48","doi-asserted-by":"publisher","unstructured":"Hu F, Zhu Y, Wu S, et al. Graphair: Graph representation learning with neighborhood aggregation and interaction. Pattern Recogn. 2021;112:107745. https:\/\/doi.org\/10.48550\/arXiv.1911.01731","DOI":"10.48550\/arXiv.1911.01731"},{"key":"9461_CR49","doi-asserted-by":"publisher","DOI":"10.1016\/j.sigpro.2021.108310","volume":"190","author":"H Hakim","year":"2022","unstructured":"Hakim H, Mounir G, Philippe C, et al. Negative sampling strategies for contrastive self-supervised learning of graph representations. Signal Process. 2022;190: 108310. https:\/\/doi.org\/10.1016\/j.sigpro.2021.108310.","journal-title":"Signal Process"},{"key":"9461_CR50","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2020.107451","volume":"106","author":"Y Luo","year":"2020","unstructured":"Luo Y, Ji R, Guan T, et al. Every node counts: Self-ensembling graph convolutional networks for semi-supervised learning. Pattern Recogn. 2020;106: 107451. https:\/\/doi.org\/10.1016\/j.patcog.2020.107451.","journal-title":"Pattern Recogn"},{"key":"9461_CR51","doi-asserted-by":"publisher","unstructured":"Peng M, Juan X and Li Z. Label-guided graph contrastive learning for semi-supervised node classification. Expert Syst Appl. 2023;239. https:\/\/doi.org\/10.1016\/j.eswa.2023.122385","DOI":"10.1016\/j.eswa.2023.122385"},{"issue":"5","key":"9461_CR52","doi-asserted-by":"publisher","first-page":"1359","DOI":"10.1007\/s00607-024-01261-6","volume":"106","author":"C Sun","year":"2024","unstructured":"Sun C, Meng F, Li C, et al. LGAT: a light graph attention network focusing on message passing for semi-supervised node classification. Computing. 2024;106(5):1359\u201393. https:\/\/doi.org\/10.1007\/s00607-024-01261-6.","journal-title":"Computing"},{"key":"9461_CR53","doi-asserted-by":"publisher","unstructured":"Feng W, Zhang J, Dong Y, et al. Graph random neural networks for semi-supervised learning on graphs. In: Proceedings of the 34th Conference on Neural Information Processing Systems. Vancouver: NeurIPS. 2020. https:\/\/doi.org\/10.48550\/arXiv.2005.11079","DOI":"10.48550\/arXiv.2005.11079"},{"key":"9461_CR54","doi-asserted-by":"publisher","unstructured":"Bo D, Hu B, Wang X, et al. Regularizing graph neural networks via consistency-diversity graph augmentations. In: Proceedings of the 36th AAAI Conference on Artificial Intelligence. Vancouver: AAAI. 2022;36:3913\u20133921. https:\/\/doi.org\/10.1609\/aaai.v36i4.20307","DOI":"10.1609\/aaai.v36i4.20307"},{"key":"9461_CR55","doi-asserted-by":"publisher","unstructured":"Hu Y, Ouyang S, Yang Z, et al. VIGraph: Generative self-supervised learning for class-imbalanced node classification. arXiv Preprint. 2024; arXiv: 2311.01191. https:\/\/doi.org\/10.48550\/arXiv.2311.01191","DOI":"10.48550\/arXiv.2311.01191"}],"container-title":["Discover Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10791-024-09461-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10791-024-09461-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10791-024-09461-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,16]],"date-time":"2024-08-16T04:18:52Z","timestamp":1723781932000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10791-024-09461-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,16]]},"references-count":55,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["9461"],"URL":"https:\/\/doi.org\/10.1007\/s10791-024-09461-6","relation":{"has-preprint":[{"id-type":"doi","id":"10.21203\/rs.3.rs-4604867\/v1","asserted-by":"object"}]},"ISSN":["2948-2992"],"issn-type":[{"value":"2948-2992","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,8,16]]},"assertion":[{"value":"19 June 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 August 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 August 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"This is original article. We confirm that this manuscript has not been published elsewhere and is not under consideration by another journal. All authors have approved the manuscript and agree with submission to Complex & Intelligent Systems.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"The authors declare no competing interests.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"26"}}