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Semi-supervised classification with graph convolutional networks. arXiv:1609.02907. 2016. doi:10.48550\/arXiv.1609.02907."},{"key":"ref18","unstructured":"Hamilton W, Ying Z, Leskovec J. Inductive representation learning on large graphs. arXiv:1706.02216. 2017. doi:10.48550\/arXiv.1706.02216."},{"key":"ref19","unstructured":"Xu K, Hu W, Leskovec J, Jegelka S. How powerful are graph neural networks? arXiv:1810.00826. 2018. doi:10.48550\/arXiv.1810.00826."},{"key":"ref20","series-title":"Proceedings of the 26th International Conference on Enterprise Information Systems; 2024 Apr 28\u201330; Angers, France","first-page":"437","article-title":"Graph convolutional networks for image classification: comparing approaches for building graphs from images","author":"Rodrigues"},{"key":"ref21","unstructured":"Han K, Wang Y, Guo J, Tang Y, Wu E. Vision GNN: an image is worth graph of nodes. arXiv:2206.00272. 2022. doi:10.48550\/arXiv.2206.00272."},{"key":"ref22","doi-asserted-by":"crossref","first-page":"5966","DOI":"10.1109\/TGRS.2020.3015157","article-title":"Graph convolutional networks for hyperspectral image classification","volume":"59","author":"Hong","year":"2021","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"ref23","doi-asserted-by":"crossref","first-page":"5483","DOI":"10.3390\/rs15235483","article-title":"Adaptive multi-feature fusion graph convolutional network for hyperspectral image classification","volume":"15","author":"Liu","year":"2023","journal-title":"Remote Sens"},{"key":"ref24","doi-asserted-by":"crossref","first-page":"8657","DOI":"10.1109\/TGRS.2020.3037361","article-title":"CNN-enhanced graph convolutional network with pixel- and superpixel-level feature fusion for hyperspectral image classification","volume":"59","author":"Liu","year":"2021","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"ref25","unstructured":"Edwards M, Xie X. Graph based convolutional neural network. arXiv:1609.08965. 2016. doi:10.48550\/arXiv.1609.08965."},{"key":"ref26","series-title":"2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW); 2021 Jun 19\u201325; Nashville, TN, USA","first-page":"2944","article-title":"Superpixels and graph convolutional neural networks for efficient detection of nutrient deficiency stress from aerial imagery","author":"Dadsetan"},{"article-title":"Image classification based on deep graph convolutional networks","series-title":"2022 IEEE 9th International Conference on Data Science and Advanced Analytics (DSAA); 2022 Oct 13\u201316; Shenzhen, China","author":"Tang","key":"ref27"},{"key":"ref28","unstructured":"Knyazev B, Lin X, Amer MR, Taylor GW. Image classification with hierarchical multigraph networks. arXiv:1907.09000. 2019. doi:10.48550\/arXiv.1907.09000."},{"key":"ref29","doi-asserted-by":"crossref","unstructured":"Wharton Z, Behera A, Bera A. 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EfficientNet: rethinking model scaling for convolutional neural networks. arXiv:1905.11946. 2019. doi:10.48550\/arXiv.1905.11946."},{"article-title":"Measuring and relieving the over-smoothing problem for graph neural networks from the topological view","series-title":"Proceedings of the AAAI Conference on Artificial Intelligence; 2020 Feb 7\u201312; New York, NY, USA","author":"Chen","key":"ref34"},{"article-title":"Deeper insights into graph convolutional networks for semi-supervised learning","series-title":"Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence and Thirtieth Innovative Applications of Artificial Intelligence Conference and Eighth AAAI Symposium on Educational Advances in Artificial Intelligence; 2018 Feb 2\u20137; New Orleans, LA, USA","author":"Li","key":"ref35"},{"key":"ref36","unstructured":"Rong Y, Huang W, Xu T, Huang J. Dropedge: towards deep graph convolutional networks on node classification. arXiv:1907.10903. 2019. doi:10.48550\/arXiv.1907.10903."},{"key":"ref37","unstructured":"Scholkemper M, Wu X, Jadbabaie A, Schaub MT. Residual connections and normalization can provably prevent oversmoothing in GNNs. arXiv:2406.02997. 2024. doi:10.48550\/arXiv.2406.02997."},{"key":"ref38","unstructured":"Wu Y. Attention is all you need for boosting graph convolutional neural network. arXiv:2403.15419. 2024. doi:10.48550\/arXiv.2403.15419."},{"key":"ref39","unstructured":"Velickovic P, Cucurull G, Casanova A, Romero A, Lio P, Bengio Y. 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