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Intell. Syst. Technol."],"published-print":{"date-parts":[[2023,6,30]]},"abstract":"<jats:p>\n            This article presents FLGC, a simple yet effective fully linear graph convolutional network for semi-supervised and unsupervised learning. Instead of using gradient descent, we train FLGC based on computing a global optimal closed-form solution with a decoupled procedure, resulting in a generalized linear framework and making it easier to implement, train, and apply. We show that (1) FLGC is powerful to deal with both graph-structured data and regular data, (2) training graph convolutional models with closed-form solutions improve computational efficiency without degrading performance, and (3) FLGC acts as a natural generalization of classic linear models in the non-Euclidean domain (e.g., ridge regression and subspace clustering). Furthermore, we implement a semi-supervised FLGC and an unsupervised FLGC by introducing an initial residual strategy, enabling FLGC to aggregate long-range neighborhoods and alleviate over-smoothing. We compare our semi-supervised and unsupervised FLGCs against many state-of-the-art methods on a variety of classification and clustering benchmarks, demonstrating that the proposed FLGC models consistently outperform previous methods in terms of accuracy, robustness, and learning efficiency. The core code of our FLGC is released at\n            <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"https:\/\/github.com\/AngryCai\/FLGC\">https:\/\/github.com\/AngryCai\/FLGC<\/jats:ext-link>\n            .\n          <\/jats:p>","DOI":"10.1145\/3579828","type":"journal-article","created":{"date-parts":[[2023,1,9]],"date-time":"2023-01-09T13:18:52Z","timestamp":1673270332000},"page":"1-23","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":8,"title":["Fully Linear Graph Convolutional Networks for Semi-Supervised and Unsupervised Classification"],"prefix":"10.1145","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2609-3036","authenticated-orcid":false,"given":"Yaoming","family":"Cai","sequence":"first","affiliation":[{"name":"Zhongnan University of Economics and Law, Wuhan, Hubei, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9034-009X","authenticated-orcid":false,"given":"Zijia","family":"Zhang","sequence":"additional","affiliation":[{"name":"China University of Geosciences, Lumo, Wuhan, Hubei, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1203-741X","authenticated-orcid":false,"given":"Pedram","family":"Ghamisi","sequence":"additional","affiliation":[{"name":"Helmholtz-Zentrum Dresden-Rossendorf (HZDR), Helmholtz Institute Freiberg for Resource Technology, Germany and Institute of Advanced Research in Artificial Intelligence (IARAI), Vienna, Austria"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0020-6503","authenticated-orcid":false,"given":"Zhihua","family":"Cai","sequence":"additional","affiliation":[{"name":"China University of Geosciences, Lumo, Wuhan, Hubei, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8298-7715","authenticated-orcid":false,"given":"Xiaobo","family":"Liu","sequence":"additional","affiliation":[{"name":"China University of Geosciences, Lumo, Wuhan, Hubei, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2040-2640","authenticated-orcid":false,"given":"Yao","family":"Ding","sequence":"additional","affiliation":[{"name":"Xi\u2019an Research Institute of High Technology, Xi\u2019an, Shaanxi, China"}]}],"member":"320","published-online":{"date-parts":[[2023,4]]},"reference":[{"key":"e_1_3_2_2_2","first-page":"21","volume-title":"Proceedings of the International Conference on Machine Learning","author":"Abu-El-Haija Sami","year":"2019","unstructured":"Sami Abu-El-Haija, Bryan Perozzi, Amol Kapoor, Nazanin Alipourfard, Kristina Lerman, Hrayr Harutyunyan, Greg Ver Steeg, and Aram Galstyan. 2019. 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