@misc{achten_semi-supervised_2023,
	title = {Semi-{Supervised} {Classification} with {Graph} {Convolutional} {Kernel} {Machines}},
	url = {http://arxiv.org/abs/2301.13764},
	doi = {10.48550/arXiv.2301.13764},
	abstract = {We present a deep Graph Convolutional Kernel Machine (GCKM) for semi-supervised node classification in graphs. First, we introduce an unsupervised kernel machine propagating the node features in a one-hop neighbourhood. Then, we specify a semi-supervised classification kernel machine through the lens of the Fenchel-Young inequality. The deep graph convolutional kernel machine is obtained by stacking multiple shallow kernel machines. After showing that unsupervised and semi-supervised layer corresponds to an eigenvalue problem and a linear system on the aggregated node features, respectively, we derive an efficient end-to-end training algorithm in the dual variables. Numerical experiments demonstrate that our approach is competitive with state-of-the-art graph neural networks for homophilious and heterophilious benchmark datasets. Notably, GCKM achieves superior performance when very few labels are available.},
	urldate = {2023-02-06},
	publisher = {arXiv},
	author = {Achten, Sonny and Tonin, Francesco and Patrinos, Panagiotis and Suykens, Johan A. K.},
	month = jan,
	year = {2023},
	note = {arXiv:2301.13764 [cs]},
	keywords = {Computer Science - Machine Learning}
}