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Found 3 papers, 2 papers with code
HeNCler: Node Clustering in Heterophilous Graphs through Learned Asymmetric Similarity
Clustering nodes in heterophilous graphs presents unique challenges due to the asymmetric relationships often overlooked by traditional methods, which moreover assume that good clustering corresponds to high intra-cluster and low inter-cluster connectivity.
Duality in Multi-View Restricted Kernel Machines
We propose a unifying setting that combines existing restricted kernel machine methods into a single primal-dual multi-view framework for kernel principal component analysis in both supervised and unsupervised settings.
Unsupervised Neighborhood Propagation Kernel Layers for Semi-supervised Node Classification
We present a deep Graph Convolutional Kernel Machine (GCKM) for semi-supervised node classification in graphs.
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