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Accurate and Fast Computation of Approximate Graph Edit Distance based on Graph RelabelingTopics: Classification; Graphical and Graph-Based Models; Similarity and Distance Learning

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Subjects/Areas/Topics:Classification
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Graphical and Graph-Based Models
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Pattern Recognition
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Similarity and Distance Learning
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Theory and Methods

Abstract: The graph edit distance, a well-known metric for determining the similarity between two graphs, is commonly
used for analyzing large sets of structured data, such as those used in chemoinformatics, document analysis,
and malware detection. As computing the exact graph edit distance is computationally expensive, and may
be intractable for large-scale datasets, various approximation techniques have been developed. In this paper,
we present a method based on graph relabeling that is both faster and more accurate than the conventional
approach. We use unfolded subtrees to denote the potential relabeling of local structures around a given vertex.
These subtree representations are concatenated as a vector, and the distance between different vectors is used
to characterize the distance between the corresponding graphs. This avoids the need for multiple calculations
of the exact graph edit distance between local structures. Simulation experiments on two real-world chemical
datasets are reported. Compared with the conventional technique, the proposed method gives a more accurate
approximation of the graph edit distance and is significantly faster on both datasets. This suggests the proposed
method could be applicable in the analysis of larger and more complex graph-like datasets.(More)

The graph edit distance, a well-known metric for determining the similarity between two graphs, is commonly used for analyzing large sets of structured data, such as those used in chemoinformatics, document analysis, and malware detection. As computing the exact graph edit distance is computationally expensive, and may be intractable for large-scale datasets, various approximation techniques have been developed. In this paper, we present a method based on graph relabeling that is both faster and more accurate than the conventional approach. We use unfolded subtrees to denote the potential relabeling of local structures around a given vertex. These subtree representations are concatenated as a vector, and the distance between different vectors is used to characterize the distance between the corresponding graphs. This avoids the need for multiple calculations of the exact graph edit distance between local structures. Simulation experiments on two real-world chemical datasets are reported. Compared with the conventional technique, the proposed method gives a more accurate approximation of the graph edit distance and is significantly faster on both datasets. This suggests the proposed method could be applicable in the analysis of larger and more complex graph-like datasets.

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Takami, S. and Inokuchi, A. (2018). Accurate and Fast Computation of Approximate Graph Edit Distance based on Graph Relabeling.In Proceedings of the 7th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-276-9, pages 17-26. DOI: 10.5220/0006540000170026

@conference{icpram18, author={Sousuke Takami. and Akihiro Inokuchi.}, title={Accurate and Fast Computation of Approximate Graph Edit Distance based on Graph Relabeling}, booktitle={Proceedings of the 7th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,}, year={2018}, pages={17-26}, publisher={SciTePress}, organization={INSTICC}, doi={10.5220/0006540000170026}, isbn={978-989-758-276-9}, }

TY - CONF

JO - Proceedings of the 7th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, TI - Accurate and Fast Computation of Approximate Graph Edit Distance based on Graph Relabeling SN - 978-989-758-276-9 AU - Takami, S. AU - Inokuchi, A. PY - 2018 SP - 17 EP - 26 DO - 10.5220/0006540000170026