mlsanityData to 14 Oct 2026

Unsupervised Neighborhood Propagation Kernel Layers for Semi-supervised Node Classification

arXiv 2301.13764 ↗First posted 31 Jan 2023cs.LGcited by 3

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Abstract (arXiv)

We present a deep Graph Convolutional Kernel Machine (GCKM) for semi-supervised node classification in graphs. The method is built of two main types of blocks: (i) We introduce unsupervised kernel machine layers propagating the node features in a one-hop neighborhood, using implicit node feature mappings. (ii) We specify a semi-supervised classification kernel machine through the lens of the Fenchel-Young inequality. We derive an effective initialization scheme and efficient end-to-end training algorithm in the dual variables for the full architecture. The main idea underlying GCKM is that, because of the unsupervised core, the final model can achieve higher performance in semi-supervised node classification when few labels are available for training. Experimental results demonstrate the effectiveness of the proposed framework.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 23 of its 49 references that are in the map. First posted on arXiv 31 Jan 2023.

Builds on

23 of 49 references.

References · 23

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  2. 2018How Powerful are Graph Neural Networks?cited by 4,393
  3. 2019A Comprehensive Survey on Graph Neural Networkscited by 2,919
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11 more
  1. 2019Graph Neural Tangent Kernel: Fusing Graph Neural Networks with Graph Kernelscited by 161
  2. 2019A Survey on Graph Kernelscited by 148
  3. 2019Wasserstein Weisfeiler-Lehman Graph Kernelscited by 104
  4. 2020Efficient Riemannian Optimization on the Stiefel Manifold via the Cayley Transformcited by 80
  5. 2022KerGNNs: Interpretable Graph Neural Networks with Graph Kernelscited by 50
  6. 2020Convolutional Kernel Networks for Graph-Structured Datacited by 38
  7. 2017Deriving Neural Architectures from Sequence and Graph Kernelscited by 23
  8. 2020Disentangled Representation Learning and Generation with Manifold Optimizationcited by 15
  9. 2020Unsupervised learning of disentangled representations in deep restricted kernel machines with orthogonality constraintscited by 9
  10. 2017Kernel Graph Convolutional Neural Networkscited by 7
  11. 2022Tensor-based Multi-view Spectral Clustering via Shared Latent Spacecited by 5

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Cited by year

Citing papers per year: 2023: 2; 2024: 1.

3 papers.

  1. 2024HeNCler: Node Clustering in Heterophilous Graphs via Learned Asymmetric Similaritycited by 2
  2. 2023Duality in Multi-View Restricted Kernel Machinescited by 1
  3. 2023Combining Primal and Dual Representations in Deep Restricted Kernel Machines Classifierscited by 0