Hadamard Code Graph Kernels for Classifying Graphs

Tetsuya Kataoka, Akihiro Inokuchi

Abstract

Kernel methods such as Support Vector Machines (SVMs) are becoming increasingly popular because of their high performance on graph classification problems. In this paper, we propose two novel graph kernels called the Hadamard Code Kernel (HCK) and the Shortened HCK (SHCK). These kernels are based on the Hadamard code, which is used in spread spectrum-based communication technologies to spread message signals. The proposed graph kernels are equivalent to the Neighborhood Hash Kernel (NHK), one of the fastest graph kernels, and comparable to the Weisfeiler-Lehman Subtree Kernel (WLSK), one of the most accurate graph kernels. The fundamental performance and practicality of the proposed graph kernels are evaluated using three real-world datasets.

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Paper Citation


in Harvard Style

Kataoka T. and Inokuchi A. (2016). Hadamard Code Graph Kernels for Classifying Graphs . In Proceedings of the 5th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-173-1, pages 24-32. DOI: 10.5220/0005634700240032


in Bibtex Style

@conference{icpram16,
author={Tetsuya Kataoka and Akihiro Inokuchi},
title={Hadamard Code Graph Kernels for Classifying Graphs},
booktitle={Proceedings of the 5th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2016},
pages={24-32},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005634700240032},
isbn={978-989-758-173-1},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 5th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Hadamard Code Graph Kernels for Classifying Graphs
SN - 978-989-758-173-1
AU - Kataoka T.
AU - Inokuchi A.
PY - 2016
SP - 24
EP - 32
DO - 10.5220/0005634700240032