Propagation kernels: efficient graph kernels from propagated information

DOI10.1007/s10994-015-5517-9zbMath1357.68178OpenAlexW1179283095WikidataQ58624307 ScholiaQ58624307MaRDI QIDQ298353

Christian Bauckhage, Marion Neumann, Roman Garnett, Kristian Kersting

Publication date: 20 June 2016

Published in: Machine Learning (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10994-015-5517-9

zbMATH Keywords

random walksconvolutionsgraph kernelslearning with graphslocality sensitive hashing

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Graph theory (including graph drawing) in computer science (68R10) Measures of information, entropy (94A17) Random walks on graphs (05C81)

Related Items (8)

GTED: Graph Traversal Edit Distance ⋮ Binary vectors for fast distance and similarity estimation ⋮ Locality preserving dense graph convolutional networks with graph context-aware node representations ⋮ The journey of graph kernels through two decades ⋮ Counts-of-counts similarity for prediction and search in relational data ⋮ A unifying view of explicit and implicit feature maps of graph kernels ⋮ Beyond graph neural networks with lifted relational neural networks ⋮ Graph Kernels: A Survey

Uses Software

• AFGen
• Benchmark Graph Kernels
• BRENDA

Cites Work

• Graph kernels based on tree patterns for molecules
• Learning Theory and Kernel Machines
• Locality-sensitive hashing scheme based on p-stable distributions
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item

This page was built for publication: Propagation kernels: efficient graph kernels from propagated information