# Faster Kernels for Graphs with Continuous Attributes via Hashing

arXiv: https://arxiv.org/abs/1610.00064 · HTML: https://mlsanity.com/paper/1610.00064/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Oct 2016.

## Built on by (most cited first, 30 of 31)

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- [Tensor-view Topological Graph Neural Network](https://mlsanity.com/paper/2401.12007/) (2024)
- [Topology-aware Reinforcement Feature Space Reconstruction for Graph Data](https://mlsanity.com/paper/2411.05742/) (2024)
- [On a linear fused Gromov-Wasserstein distance for graph structured data](https://mlsanity.com/paper/2203.04711/) (2022)
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- [RAHNet: Retrieval Augmented Hybrid Network for Long-tailed Graph Classification](https://mlsanity.com/paper/2308.02335/) (2023)
- [Gaussian process regression with Sliced Wasserstein Weisfeiler-Lehman graph kernels](https://mlsanity.com/paper/2402.03838/) (2024)
- [Investigating Transfer Learning in Graph Neural Networks](https://mlsanity.com/paper/2202.00740/) (2022)
- [Cluster-guided Contrastive Class-imbalanced Graph Classification](https://mlsanity.com/paper/2412.12984/) (2024)
- [Gradual Weisfeiler-Leman: Slow and Steady Wins the Race](https://mlsanity.com/paper/2209.09048/) (2022)
- [Towards Long-Tailed Recognition for Graph Classification via Collaborative Experts](https://mlsanity.com/paper/2308.16609/) (2023)
- [Descriptive Kernel Convolution Network with Improved Random Walk Kernel](https://mlsanity.com/paper/2402.06087/) (2024)
- [Graph Learning for Numeric Planning](https://mlsanity.com/paper/2410.24080/) (2024)
- [EMP: Effective Multidimensional Persistence for Graph Representation Learning](https://mlsanity.com/paper/2401.13713/) (2024)
- [The PWLR Graph Representation: A Persistent Weisfeiler-Lehman scheme with Random Walks for Graph Classification](https://mlsanity.com/paper/2208.13427/) (2022)
- [A Regularized Wasserstein Framework for Graph Kernels](https://mlsanity.com/paper/2110.02554/) (2021)
- [Data Imputation from the Perspective of Graph Dirichlet Energy](https://mlsanity.com/paper/2304.04474/) (2023)
- [WLPlan: Relational Features for Symbolic Planning](https://mlsanity.com/paper/2411.00577/) (2024)
- [Rhomboid Tiling for Geometric Graph Deep Learning](https://mlsanity.com/paper/2505.09586/) (2025)
- [A systematic approach to random data augmentation on graph neural networks](https://mlsanity.com/paper/2112.04314/) (2021)
- [Metric Distribution to Vector: Constructing Data Representation via Broad-Scale Discrepancies](https://mlsanity.com/paper/2210.00415/) (2022)
- [DPGAN: A Dual-Path Generative Adversarial Network for Missing Data Imputation in Graphs](https://mlsanity.com/paper/2404.17164/) (2024)
- [Dual Boost-Driven Graph-Level Clustering Network](https://mlsanity.com/paper/2504.05670/) (2025)
- [Optimization-Free Graph Embedding via Distributional Kernel for Community Detection](https://mlsanity.com/paper/2602.13634/) (2026)

## Abstract (arXiv)

While state-of-the-art kernels for graphs with discrete labels scale well to graphs with thousands of nodes, the few existing kernels for graphs with continuous attributes, unfortunately, do not scale well. To overcome this limitation, we present hash graph kernels, a general framework to derive kernels for graphs with continuous attributes from discrete ones. The idea is to iteratively turn continuous attributes into discrete labels using randomized hash functions. We illustrate hash graph kernels for the Weisfeiler-Lehman subtree kernel and for the shortest-path kernel. The resulting novel graph kernels are shown to be, both, able to handle graphs with continuous attributes and scalable to large graphs and data sets. This is supported by our theoretical analysis and demonstrated by an extensive experimental evaluation.