{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,24]],"date-time":"2026-06-24T08:08:37Z","timestamp":1782288517568,"version":"3.54.5"},"reference-count":80,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"1","license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61932022"],"award-info":[{"award-number":["61932022"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61931023"],"award-info":[{"award-number":["61931023"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61971285"],"award-info":[{"award-number":["61971285"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61831018"],"award-info":[{"award-number":["61831018"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61871267"],"award-info":[{"award-number":["61871267"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61720106001"],"award-info":[{"award-number":["61720106001"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62120106007"],"award-info":[{"award-number":["62120106007"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61972256"],"award-info":[{"award-number":["61972256"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["T2122024"],"award-info":[{"award-number":["T2122024"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62125109"],"award-info":[{"award-number":["62125109"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100019445","name":"Program of Shanghai Science and Technology Innovation Project","doi-asserted-by":"publisher","award":["20511100100"],"award-info":[{"award-number":["20511100100"]}],"id":[{"id":"10.13039\/501100019445","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Central Hardware Engineering Institute of Huawei"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Neural Netw. Learning Syst."],"published-print":{"date-parts":[[2024,1]]},"DOI":"10.1109\/tnnls.2022.3179306","type":"journal-article","created":{"date-parts":[[2022,6,9]],"date-time":"2022-06-09T20:35:36Z","timestamp":1654806936000},"page":"1025-1039","source":"Crossref","is-referenced-by-count":31,"title":["NCGNN: Node-Level Capsule Graph Neural Network for Semisupervised Classification"],"prefix":"10.1109","volume":"35","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4552-584X","authenticated-orcid":false,"given":"Rui","family":"Yang","sequence":"first","affiliation":[{"name":"Department of Electronic Engineering, Shanghai Jiao Tong University, Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2522-5778","authenticated-orcid":false,"given":"Wenrui","family":"Dai","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2888-594X","authenticated-orcid":false,"given":"Chenglin","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Electronic Engineering, Shanghai Jiao Tong University, Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9694-9880","authenticated-orcid":false,"given":"Junni","family":"Zou","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4552-0029","authenticated-orcid":false,"given":"Hongkai","family":"Xiong","sequence":"additional","affiliation":[{"name":"Department of Electronic Engineering, Shanghai Jiao Tong University, Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","first-page":"1","article-title":"Semi-supervised classification with graph convolutional networks","volume-title":"Proc. 5th Int. Conf. Learn. Rep.","author":"Kipf"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3219947"},{"key":"ref3","first-page":"1","article-title":"Graph attention networks","volume-title":"Proc. 6th Int. Conf. Learn. Rep.","author":"Veli\u010dkovi\u0107"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.11"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1145\/2751541"},{"key":"ref6","first-page":"2224","article-title":"Convolutional networks on graphs for learning molecular fingerprints","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Duvenaud"},{"key":"ref7","first-page":"1263","article-title":"Neural message passing for quantum chemistry","volume-title":"Proc. 34th Int. Conf. Mach. Learn.","author":"Gilmer"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3219890"},{"key":"ref9","first-page":"1","article-title":"Adaptive structural fingerprints for graph attention networks","volume-title":"Proc. 8th Int. Conf. Learn. Rep.","author":"Zhang"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00943"},{"key":"ref11","first-page":"1","article-title":"Composition-based multi-relational graph convolutional networks","volume-title":"Proc. 8th Int. Conf. Learn. Rep.","author":"Vashishth"},{"key":"ref12","first-page":"1","article-title":"Geom-GCN: Geometric graph convolutional networks","volume-title":"Proc. 8th Int. Conf. Learn. Rep.","author":"Pei"},{"key":"ref13","first-page":"1","article-title":"Curvature graph network","volume-title":"Proc. 8th Int. Conf. Learn. Rep.","author":"Ye"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1146\/annurev.soc.27.1.415"},{"key":"ref15","first-page":"9244","article-title":"GNNExplainer: Generating explanations for graph neural networks","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Ying"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403085"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.11604"},{"key":"ref18","first-page":"6530","article-title":"Protein interface prediction using graph convolutional networks","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Fout"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-21735-7_6"},{"key":"ref20","first-page":"3856","article-title":"Dynamic routing between capsules","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Sabour"},{"key":"ref21","first-page":"1","article-title":"Matrix capsules with EM routing","volume-title":"Proc. 6th Int. Conf. Learn. Rep.","author":"Hinton"},{"key":"ref22","first-page":"1","article-title":"Group equivariant capsule networks","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Lenssen"},{"key":"ref23","first-page":"1","article-title":"Capsules with inverted dot-product attention routing","volume-title":"Proc. 8th Int. Conf. Learn. Rep.","author":"Tsai"},{"key":"ref24","first-page":"1","article-title":"Graph capsule convolutional neural networks","volume-title":"Proc. Joint ICML IJCAI Workshop Comp. Biol.","author":"Verma"},{"key":"ref25","article-title":"Capsule neural networks for graph classification using explicit tensorial graph representations","author":"Mallea","year":"2019","journal-title":"arXiv:1902.08399"},{"key":"ref26","first-page":"1","article-title":"Capsule graph neural network","volume-title":"Proc. 7th Int. Conf. Learn. Rep.","author":"Xinyi"},{"key":"ref27","first-page":"4212","article-title":"Disentangled graph convolutional networks","volume-title":"Proc. 36th Int. Conf. Mach. Learn.","author":"Ma"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1145\/3340531.3417455"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i12.17268"},{"key":"ref30","first-page":"7793","article-title":"Beyond homophily in graph neural networks: Current limitations and effective designs","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Zhu"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1609\/aimag.v29i3.2157"},{"key":"ref32","first-page":"1","article-title":"Query-driven active surveying for collective classification","volume-title":"Proc. ICML Workshop Mining Learn. Graphs","author":"Namata"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1145\/3097983.3098061"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i04.5747"},{"key":"ref35","first-page":"5453","article-title":"Representation learning on graphs with jumping knowledge networks","volume-title":"Proc. 35th Int. Conf. Mach. Learn.","author":"Xu"},{"key":"ref36","first-page":"1","article-title":"Just jump: Dynamic neighborhood aggregation in graph neural networks","volume-title":"Proc. ICLR Workshop Rep. Learn. Graphs Manifolds","author":"Fey"},{"key":"ref37","first-page":"1","article-title":"Predict then propagate: Graph neural networks meet personalized PageRank","volume-title":"Proc. 7th Int. Conf. Learn. Rep.","author":"Klicpera"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00936"},{"key":"ref39","first-page":"1725","article-title":"Simple and deep graph convolutional networks","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Chen"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403076"},{"key":"ref41","first-page":"1","article-title":"Scattering GCN: Overcoming oversmoothness in graph convolutional networks","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Min"},{"key":"ref42","first-page":"1","article-title":"PairNorm: Tackling oversmoothing in GNNs","volume-title":"Proc. 8th Int. Conf. Learn. Rep.","author":"Zhao"},{"key":"ref43","first-page":"4917","article-title":"Towards deeper graph neural networks with differentiable group normalization","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Zhou"},{"key":"ref44","first-page":"1","article-title":"DropEdge: Towards deep graph convolutional networks on node classification","volume-title":"Proc. 8th Int. Conf. Learn. Rep.","author":"Rong"},{"key":"ref45","first-page":"4094","article-title":"Bayesian graph neural networks with adaptive connection sampling","volume-title":"Proc. 37th Int. Conf. Mach. Learn.","author":"Hasanzadeh"},{"issue":"1","key":"ref46","first-page":"1929","article-title":"DropOut: A simple way to prevent neural networks from overfitting","volume":"15","author":"Srivastava","year":"2014","journal-title":"J. Mach. Learn. Res."},{"key":"ref47","first-page":"1","article-title":"Explainability techniques for graph convolutional networks","volume-title":"Proc. ICML Workshop Learn. Reason. Graph-Structured Rep.","author":"Baldassarre"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1016\/S0304-3800(02)00257-0"},{"key":"ref49","first-page":"1","article-title":"Striving for simplicity: The all convolutional net","volume-title":"Proc. 3rd Int. Conf. Learn. Rep. (Workshop Track)","author":"Springenberg"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0130140"},{"key":"ref51","article-title":"Deep inside convolutional networks: Visualising image classification models and saliency maps","author":"Simonyan","year":"2013","journal-title":"arXiv:1312.6034"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.319"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-017-1059-x"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.01103"},{"key":"ref55","first-page":"12225","article-title":"PGM-Explainer: Probabilistic graphical model explanations for graph neural networks","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Vu"},{"key":"ref56","first-page":"19620","article-title":"Parameterized explainer for graph neural network","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Luo"},{"key":"ref57","first-page":"1","article-title":"Reinforcement learning enhanced explainer for graph neural networks","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Shan"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2021.3115452"},{"key":"ref59","first-page":"12241","article-title":"On explainability of graph neural networks via subgraph explorations","volume-title":"Proc. 38th Int. Conf. Mach. Learn.","author":"Yuan"},{"key":"ref60","first-page":"6666","article-title":"Generative causal explanations for graph neural networks","volume-title":"Proc. 38th Int. Conf. Mach. Learn.","author":"Lin"},{"key":"ref61","first-page":"1","article-title":"CF-GNNExplainer: Counterfactual explanations for graph neural networks","volume-title":"Proc. KDD Deep Learn. Graphs (DLG) Workshop","author":"Lucic"},{"key":"ref62","first-page":"1","article-title":"Robust counterfactual explanations on graph neural networks","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Bajaj"},{"key":"ref63","first-page":"1","article-title":"Towards multi-grained explainability for graph neural networks","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Wang"},{"key":"ref64","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2021.3101356"},{"key":"ref65","doi-asserted-by":"publisher","DOI":"10.1038\/s42256-020-00244-4"},{"key":"ref66","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.576"},{"key":"ref67","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i04.5929"},{"key":"ref68","first-page":"21","article-title":"Mixhop: Higher-order graph convolutional architectures via sparsified neighborhood mixing","volume-title":"Proc. Int. Conf. Mach. Learn. (ICML)","author":"Abu-El-Haija"},{"key":"ref69","doi-asserted-by":"publisher","DOI":"10.1145\/3357384.3357880"},{"key":"ref70","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4419-9863-7_2"},{"key":"ref71","first-page":"13354","article-title":"Diffusion improves graph learning","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Klicpera"},{"key":"ref72","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403296"},{"issue":"2","key":"ref73","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1023\/A:1009953814988","article-title":"Automating the construction of internet portals with machine learning","volume":"3","author":"McCallum","year":"2000","journal-title":"Inf. Retr."},{"key":"ref74","first-page":"1","article-title":"Deep Gaussian embedding of graphs: Unsupervised inductive learning via ranking","volume-title":"Proc. 6th Int. Conf. Learn. Rep.","author":"Bojchevski"},{"key":"ref75","first-page":"1","article-title":"Pitfalls of graph neural network evaluation","volume-title":"Proc. Relat. Rep. Learn. Workshop (NIPS)","author":"Shchur"},{"key":"ref76","article-title":"Multi-scale attributed node embedding","author":"Rozemberczki","year":"2019","journal-title":"arXiv:1909.13021"},{"key":"ref77","doi-asserted-by":"publisher","DOI":"10.1145\/1557019.1557108"},{"key":"ref78","first-page":"6861","article-title":"Simplifying graph convolutional networks","volume-title":"Proc. ICML","author":"Wu"},{"key":"ref79","first-page":"1","article-title":"Adam: A method for stochastic optimization","volume-title":"Proc. 3rd Int. Conf. Learn. Rep.","author":"Kingma"},{"key":"ref80","doi-asserted-by":"publisher","DOI":"10.1007\/s10479-011-0841-3"}],"container-title":["IEEE Transactions on Neural Networks and Learning Systems"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/5962385\/10381493\/09792205.pdf?arnumber=9792205","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,12]],"date-time":"2024-01-12T00:13:51Z","timestamp":1705018431000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9792205\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1]]},"references-count":80,"journal-issue":{"issue":"1"},"URL":"https:\/\/doi.org\/10.1109\/tnnls.2022.3179306","relation":{},"ISSN":["2162-237X","2162-2388"],"issn-type":[{"value":"2162-237X","type":"print"},{"value":"2162-2388","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,1]]}}}