Fast depth-based subgraph kernels for unattributed graphs

DOI10.1016/j.patcog.2015.08.006zbMath1394.68272OpenAlexW1669094309WikidataQ60430828 ScholiaQ60430828MaRDI QIDQ1669713

Edwin R. Hancock, Lu Bai

Publication date: 4 September 2018

Published in: Pattern Recognition (Search for Journal in Brave)

Full work available at URL: https://eprints.whiterose.ac.uk/92418/1/PR_D_14_00925R1.pdf

zbMATH Keywords

entropygraph kernelsJensen-Shannon divergencedepth-based representationsgraph isomorphism tests

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) Machine vision and scene understanding (68T45)

Related Items (3)

A Nested Alignment Graph Kernel Through the Dynamic Time Warping Framework ⋮ Adaptive Feature Selection Based on the Most Informative Graph-Based Features ⋮ A combined Weisfeiler–Lehman graph kernel for structured data

Uses Software

• AFGen

Cites Work

• Unnamed Item
• Graph kernels from the Jensen-Shannon divergence
• Quantum walks, Ihara zeta functions and cospectrality in regular graphs
• Graph matching using the interference of continuous-time quantum walks
• A quantum Jensen-Shannon graph kernel for unattributed graphs
• Graph Classification and Clustering Based on Vector Space Embedding
• Effective graph classification based on topological and label attributes
• Graph-Theoretical Constructions for Graph Entropy and Network Coding Based Communications
• Learning Theory and Kernel Machines
• Discrete Geometry for Computer Imagery

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