{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T00:48:27Z","timestamp":1760230107662,"version":"build-2065373602"},"reference-count":57,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2022,7,8]],"date-time":"2022-07-08T00:00:00Z","timestamp":1657238400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001863","name":"New Energy and Industrial Technology Development Organization (NEDO)","doi-asserted-by":"publisher","award":["JPNP20006"],"award-info":[{"award-number":["JPNP20006"]}],"id":[{"id":"10.13039\/501100001863","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Deep learning algorithms have seen a massive rise in popularity for remote sensing over the past few years. Recently, studies on applying deep learning techniques to graph data in remote sensing (e.g., public transport networks) have been conducted. In graph node classification tasks, traditional graph neural network (GNN) models assume that different types of misclassifications have an equal loss and thus seek to maximize the posterior probability of the sample nodes under labeled classes. The graph data used in realistic scenarios tend to follow unbalanced long-tailed class distributions, where a few majority classes contain most of the vertices and the minority classes contain only a small number of nodes, making it difficult for the GNN to accurately predict the minority class samples owing to the classification tendency of the majority classes. In this paper, we propose a dual cost-sensitive graph convolutional network (DCSGCN) model. The DCSGCN is a two-tower model containing two subnetworks that compute the posterior probability and the misclassification cost. The model uses the cost as \u201dcomplementary information\u201d in a prediction to correct the posterior probability under the perspective of minimal risk. Furthermore, we propose a new method for computing the node cost labels based on topological graph information and the node class distribution. The results of extensive experiments demonstrate that DCSGCN outperformed other competitive baselines on different real-world imbalanced long-tailed graphs.<\/jats:p>","DOI":"10.3390\/rs14143295","type":"journal-article","created":{"date-parts":[[2022,7,11]],"date-time":"2022-07-11T00:06:21Z","timestamp":1657497981000},"page":"3295","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Long-Tailed Graph Representation Learning via Dual Cost-Sensitive Graph Convolutional Network"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5098-8593","authenticated-orcid":false,"given":"Yijun","family":"Duan","sequence":"first","affiliation":[{"name":"National Institute of Advanced Industrial Science and Technology Tokyo Waterfront, 2 Chome-3-26 Aomi, Koto City, Tokyo 135-0064, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2336-7409","authenticated-orcid":false,"given":"Xin","family":"Liu","sequence":"additional","affiliation":[{"name":"National Institute of Advanced Industrial Science and Technology Tokyo Waterfront, 2 Chome-3-26 Aomi, Koto City, Tokyo 135-0064, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Adam","family":"Jatowt","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Innsbruck, Innrain 52, 6020 Innsbruck, Austria"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1569-8507","authenticated-orcid":false,"given":"Hai-tao","family":"Yu","sequence":"additional","affiliation":[{"name":"Faculty of Library, Information and Media Science, University of Tsukuba, 1 Chome-1-1 Tennodai, Tsukuba 305-8577, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Steven","family":"Lynden","sequence":"additional","affiliation":[{"name":"National Institute of Advanced Industrial Science and Technology Tokyo Waterfront, 2 Chome-3-26 Aomi, Koto City, Tokyo 135-0064, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kyoung-Sook","family":"Kim","sequence":"additional","affiliation":[{"name":"National Institute of Advanced Industrial Science and Technology Tokyo Waterfront, 2 Chome-3-26 Aomi, Koto City, Tokyo 135-0064, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Akiyoshi","family":"Matono","sequence":"additional","affiliation":[{"name":"National Institute of Advanced Industrial Science and Technology Tokyo Waterfront, 2 Chome-3-26 Aomi, Koto City, Tokyo 135-0064, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,7,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Ma, Y.T. 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