mlsanityData to 13 Oct 2026

The Multiscale Laplacian Graph Kernel

arXiv 1603.06186 ↗First posted 20 Mar 2016stat.MLcited by 31

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Abstract (arXiv)

Many real world graphs, such as the graphs of molecules, exhibit structure at multiple different scales, but most existing kernels between graphs are either purely local or purely global in character. In contrast, by building a hierarchy of nested subgraphs, the Multiscale Laplacian Graph kernels (MLG kernels) that we define in this paper can account for structure at a range of different scales. At the heart of the MLG construction is another new graph kernel, called the Feature Space Laplacian Graph kernel (FLG kernel), which has the property that it can lift a base kernel defined on the vertices of two graphs to a kernel between the graphs. The MLG kernel applies such FLG kernels to subgraphs recursively. To make the MLG kernel computationally feasible, we also introduce a randomized projection procedure, similar to the Nystr"om method, but for RKHS operators.

Key facts. Cited by 31 papers in the map (data through 13 Oct 2026). First posted on arXiv 20 Mar 2016.

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Citing papers per year: 2021: 10; 2022: 7; 2023: 3; 2024: 7; 2025: 2; 2026: 2.

31 papers.

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  1. 2021Weisfeiler and Leman go Machine Learning: The Story so farcited by 121
  2. 2021InfoGCL: Information-Aware Graph Contrastive Learningcited by 105
  3. 2021Prototypical Graph Contrastive Learningcited by 37
  4. 2022SpeqNets: Sparsity-aware Permutation-equivariant Graph Networkscited by 37
  5. 2022GraphHD: Efficient graph classification using hyperdimensional computingcited by 28
  6. 2022Learning Graph Augmentations to Learn Graph Representationscited by 14
  7. 2022Learning Disentangled Textual Representations via Statistical Measures of Similaritycited by 14
  8. 2021Label Contrastive Coding based Graph Neural Network for Graph Classificationcited by 10
  9. 2024The GeometricKernels Package: Heat and Mat'ern Kernels for Geometric Learning on Manifolds, Meshes, and Graphscited by 10
  10. 2021Graphon based Clustering and Testing of Networks: Algorithms and Theorycited by 7
  11. 2021Learning Graph Representation by Aggregating Subgraphs via Mutual Information Maximizationcited by 5
  12. 2021Learning Graphon Autoencoders for Generative Graph Modelingcited by 5
19 more
  1. 2021Computing Graph Descriptors on Edge Streamscited by 4
  2. 2023Graph Classification Gaussian Processes via Spectral Featurescited by 4
  3. 2024EMP: Effective Multidimensional Persistence for Graph Representation Learningcited by 4
  4. 2024Improving Graph Machine Learning Performance Through Feature Augmentation Based on Network Control Theorycited by 4
  5. 2021Self-Supervised Graph Representation Learning via Topology Transformationscited by 3
  6. 2023Deep Graph-Level Clustering Using Pseudo-Label-Guided Mutual Information Maximization Networkcited by 3
  7. 2024DisenSemi: Semi-supervised Graph Classification via Disentangled Representation Learningcited by 3
  8. 2026Minimax Lower Bounds of Kernel Discrepancy Estimation: MMD, HSIC, KSDcited by 2
  9. 2021Revisiting 2D Convolutional Neural Networks for Graph-based Applicationscited by 1
  10. 2022Multi-scale Wasserstein Shortest-path Graph Kernels for Graph Classificationcited by 1
  11. 2024Learning Attributed Graphlets: Predictive Graph Mining by Graphlets with Trainable Attributecited by 1
  12. 2022A Temporal Graphlet Kernel for Classifying Dissemination in Evolving Networkscited by 0
  13. 2022HCL: Improving Graph Representation with Hierarchical Contrastive Learningcited by 0
  14. 2023Randomized Schur Complement Views for Graph Contrastive Learningcited by 0
  15. 2024Graph-level Protein Representation Learning by Structure Knowledge Refinementcited by 0
  16. 2024SPGNN: Recognizing Salient Subgraph Patterns via Enhanced Graph Convolution and Poolingcited by 0
  17. 2025Feature Construction Using Network Control Theory and Rank Encoding for Graph Machine Learningcited by 0
  18. 2025Explainable Graph Representation Learning via Graph Pattern Analysiscited by 0
  19. 2026Lighting-aware Unified Model for Instance Segmentationcited by 0