{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T20:49:18Z","timestamp":1780606158099,"version":"3.54.1"},"reference-count":57,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2024,4,4]],"date-time":"2024-04-04T00:00:00Z","timestamp":1712188800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2020YFC1523300"],"award-info":[{"award-number":["2020YFC1523300"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Artif. Intell."],"abstract":"<jats:p>The dual-channel graph convolutional neural networks based on hybrid features jointly model the different features of networks, so that the features can learn each other and improve the performance of various subsequent machine learning tasks. However, current dual-channel graph convolutional neural networks are limited by the number of convolution layers, which hinders the performance improvement of the models. Graph convolutional neural networks superimpose multi-layer graph convolution operations, which would occur in smoothing phenomena, resulting in performance decreasing as the increasing number of graph convolutional layers. Inspired by the success of residual connections on convolutional neural networks, this paper applies residual connections to dual-channel graph convolutional neural networks, and increases the depth of dual-channel graph convolutional neural networks. Thus, a dual-channel deep graph convolutional neural network (D2GCN) is proposed, which can effectively avoid over-smoothing and improve model performance. D2GCN is verified on CiteSeer, DBLP, and SDBLP datasets, the results show that D2GCN performs better than the comparison algorithms used in node classification tasks.<\/jats:p>","DOI":"10.3389\/frai.2024.1290491","type":"journal-article","created":{"date-parts":[[2024,4,4]],"date-time":"2024-04-04T14:15:06Z","timestamp":1712240106000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":10,"title":["Dual-channel deep graph convolutional neural networks"],"prefix":"10.3389","volume":"7","author":[{"given":"Zhonglin","family":"Ye","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhuoran","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gege","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haixing","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2024,4,4]]},"reference":[{"key":"ref1","author":"Abu-El-Haija","year":"2019"},{"key":"ref2","author":"Armeni","year":"2017"},{"key":"ref3","author":"Atwood","year":"2016"},{"key":"ref4","author":"Bastings","year":"2017"},{"key":"ref5","author":"Bruna","year":"2014"},{"key":"ref6","doi-asserted-by":"publisher","first-page":"863","DOI":"10.1007\/s00371-021-02351-8","article-title":"DDGCN: graph convolution network based on direction and distance for point cloud learning","volume":"39","author":"Chen","year":"2023","journal-title":"Vis. Comput."},{"key":"ref7","doi-asserted-by":"publisher","first-page":"129","DOI":"10.1016\/j.acha.2010.04.005","article-title":"Wavelets on graphs via spectral graph theory","volume":"30","author":"David","year":"2011","journal-title":"Appl. Comput. Harmon. Anal."},{"key":"ref8","author":"Defferrard","year":"2016"},{"key":"ref9","doi-asserted-by":"publisher","first-page":"43251","DOI":"10.1007\/s11042-023-14885-1","article-title":"Affect-GCN: a multimodal graph convolutional network for multi-emotion with intensity recognition and sentiment analysis in dialogues","volume":"82","author":"Firdaus","year":"2023","journal-title":"Multimed. Tools Appl."},{"key":"ref10","author":"Gilmer","year":"2017"},{"key":"ref11","author":"Hamilton","year":"2017"},{"key":"ref12","author":"He","year":"2016"},{"key":"ref13","first-page":"82","article-title":"Deep neural networks for acoustic modeling in speech recognition: the shared views of four research groups","volume-title":"IEEE Signal Process. Mag.","author":"Hinton","year":"2012"},{"key":"ref14","doi-asserted-by":"publisher","first-page":"1015","DOI":"10.1109\/JAS.2021.1003976","article-title":"ST-trader: a spatial-temporal deep neural network for modeling stock market movement","volume":"8","author":"Hou","year":"2021","journal-title":"IEEE\/CAA J Automat Sin"},{"key":"ref15","author":"Hu","year":"2014"},{"key":"ref16","author":"Huang","year":"2017"},{"key":"ref17","doi-asserted-by":"publisher","first-page":"110125","DOI":"10.1016\/j.knosys.2022.110125","article-title":"CRF-GCN: An effective syntactic dependency model for aspect-level sentiment analysis","volume":"260","author":"Huang","year":"2023","journal-title":"Knowl.-Based Syst."},{"key":"ref18","author":"Jain","year":"2016"},{"key":"ref19","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s00371-023-02921-y","article-title":"Unsupervised contrastive learning with simple transformation for 3D point cloud data","author":"Jiang","year":"2023","journal-title":"Vis. Comput."},{"key":"ref20","author":"Khodadad","year":"2023"},{"key":"ref21","author":"Kipf","year":"2017"},{"key":"ref22","author":"Klicpera","year":""},{"key":"ref23","author":"Klicpera","year":""},{"key":"ref24","author":"Li","year":"2018"},{"key":"ref25","author":"Li","year":"2019"},{"key":"ref26","author":"Li","year":"2018"},{"key":"ref27","doi-asserted-by":"publisher","first-page":"14798","DOI":"10.1109\/JIOT.2021.3091883","article-title":"Dual mutual robust graph convolutional network for weakly supervised node classification in social networks of internet of people[J]","volume":"10","author":"Li","year":"2023","journal-title":"IEEE Internet Things J."},{"key":"ref28","author":"Marcheggiani","year":"2017"},{"key":"ref29","author":"Monti","year":""},{"key":"ref30","author":"Monti","year":""},{"key":"ref31","author":"Morris","year":"2019"},{"key":"ref32","author":"Page","year":"1998"},{"key":"ref33","author":"Pham","year":"2017"},{"key":"ref34","author":"Qi","year":"2017"},{"key":"ref35","author":"Rahimi","year":"2018"},{"key":"ref36","author":"Rong","year":"2019"},{"key":"ref37","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1109\/TNN.2008.2005605","article-title":"The graph neural network model","volume":"20","author":"Scarselli","year":"2009","journal-title":"IEEE Trans. Neural Netw."},{"key":"ref38","author":"Tang","year":"2009"},{"key":"ref39","doi-asserted-by":"publisher","first-page":"347","DOI":"10.1007\/s10115-007-0103-5","article-title":"Comparison of descriptor spaces for chemical compound retrieval and classification","volume":"14","author":"Wale","year":"2008","journal-title":"Knowl. Inf. Syst."},{"key":"ref40","doi-asserted-by":"publisher","first-page":"330","DOI":"10.1109\/JIOT.2022.3200964","article-title":"Minority-weighted graph neural network for imbalanced node classification in social networks of internet of people[J]","volume":"10","author":"Wang","year":"2023","journal-title":"IEEE Internet Things J."},{"key":"ref41","doi-asserted-by":"publisher","first-page":"110080","DOI":"10.1016\/j.knosys.2022.110080","article-title":"SAT-GCN: self-attention graph convolutional network-based 3D object detection for autonomous driving","volume":"259","author":"Wang","year":"2023","journal-title":"Knowl.-Based Syst."},{"key":"ref42","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3326362","article-title":"Dynamic graph CNN for learning on point clouds","volume":"38","author":"Wang","year":"2019","journal-title":"ACM Trans. Graph."},{"key":"ref43","author":"Wu","year":"2019"},{"key":"ref44","author":"Xu","year":"2018"},{"key":"ref45","author":"Xu","year":"2017"},{"key":"ref46","author":"Yan","year":"2018"},{"key":"ref47","doi-asserted-by":"publisher","first-page":"123","DOI":"10.1109\/TSIPN.2023.3244112","article-title":"Tackling over-smoothing in graph convolutional networks with EM-based joint topology optimization and node classification","volume":"9","author":"Yang","year":"2023","journal-title":"IEEE Trans. Signal Info. Process. Netw."},{"key":"ref48","author":"Yang","year":"2018"},{"key":"ref49","author":"Ying","year":"2018"},{"key":"ref50","author":"Yu","year":"2015"},{"key":"ref51","doi-asserted-by":"publisher","first-page":"4273","DOI":"10.3390\/math10224273","article-title":"Knowledge-enhanced Dual-Channel GCN for aspect-based sentiment analysis","volume":"10","author":"Zhang","year":"2022","journal-title":"Mathematics"},{"key":"ref52","author":"Zhang","year":"2023"},{"key":"ref53","author":"Zhao","year":"2020"},{"key":"ref54","doi-asserted-by":"publisher","first-page":"9019","DOI":"10.1109\/TKDE.2022.3220789","article-title":"Dual feature interaction-based graph convolutional network","volume":"35","author":"Zhao","year":"2023","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref55","author":"Zhou","year":"2018"},{"key":"ref56","doi-asserted-by":"publisher","first-page":"1640","DOI":"10.1109\/JAS.2023.123369","article-title":"RGCNU: recurrent graph convolutional network with uncertainty estimation for remaining useful life prediction","volume":"10","author":"Zhu","year":"2023","journal-title":"IEEE\/CAA J Automat Sin"},{"key":"ref57","doi-asserted-by":"publisher","first-page":"i190","DOI":"10.1093\/bioinformatics\/btx252","article-title":"Predicting multicellular function through multi-layer tissue networks","volume":"33","author":"Zitnik","year":"2017","journal-title":"Bioinformatics"}],"container-title":["Frontiers in Artificial Intelligence"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/frai.2024.1290491\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,4,4]],"date-time":"2024-04-04T14:15:10Z","timestamp":1712240110000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/frai.2024.1290491\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,4,4]]},"references-count":57,"alternative-id":["10.3389\/frai.2024.1290491"],"URL":"https:\/\/doi.org\/10.3389\/frai.2024.1290491","relation":{},"ISSN":["2624-8212"],"issn-type":[{"value":"2624-8212","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,4,4]]},"article-number":"1290491"}}