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Authors: Anthony Gillioz and Kaspar Riesen

Affiliation: Institute of Computer Science, University of Bern, Switzerland

Keyword(s): Structural Pattern Recognition, Graph Matching, Centrality Measures, Hierarchical Graph Matching.

Abstract: A common method to solve the non-trivial task of classifying general graphs is to employ graph matching in conjunction with a distance- or similarity-based classifier. Unfortunately, optimal graph matching has a high computational complexity hindering its application on large graphs. In order to make matchings also feasible for larger graphs, it has been proposed to work on size-reduced graphs rather than on their original counterparts. In the present paper, we propose a novel method that is based on this idea to further reduce the processing time. In particular, we change the standard classification scheme into a two-step classification method. In the first step, we start with strongly reduced versions of the graphs – having a manageable amount of nodes – in order to prune as many graphs as possible. The second step – the actual classification – is then performed on the remaining graphs only (in their original size). We conduct experimental evaluations on five datasets to research t he benefits and limitations of this novel two-step graph classification method. The main finding is that we can substantially speed up the graph matching while preserving satisfying classification accuracy. (More)

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Paper citation in several formats:
Gillioz, A. and Riesen, K. (2023). Two-Step Graph Classification on the Basis of Hierarchical Graphs. In Proceedings of the 12th International Conference on Pattern Recognition Applications and Methods - ICPRAM; ISBN 978-989-758-626-2; ISSN 2184-4313, SciTePress, pages 296-303. DOI: 10.5220/0011657000003411

@conference{icpram23,
author={Anthony Gillioz and Kaspar Riesen},
title={Two-Step Graph Classification on the Basis of Hierarchical Graphs},
booktitle={Proceedings of the 12th International Conference on Pattern Recognition Applications and Methods - ICPRAM},
year={2023},
pages={296-303},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011657000003411},
isbn={978-989-758-626-2},
issn={2184-4313},
}

TY - CONF

JO - Proceedings of the 12th International Conference on Pattern Recognition Applications and Methods - ICPRAM
TI - Two-Step Graph Classification on the Basis of Hierarchical Graphs
SN - 978-989-758-626-2
IS - 2184-4313
AU - Gillioz, A.
AU - Riesen, K.
PY - 2023
SP - 296
EP - 303
DO - 10.5220/0011657000003411
PB - SciTePress

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