Generation of graph embedding vectors based on graph isomorphism problem

Yosuke Higuchi; Yoshimitsu Kuroki

doi:10.1117/12.3019647

2 May 2024 Generation of graph embedding vectors based on graph isomorphism problem

Yosuke Higuchi, Yoshimitsu Kuroki

Proceedings Volume 13164, International Workshop on Advanced Imaging Technology (IWAIT) 2024; 1316432 (2024) https://doi.org/10.1117/12.3019647
Event: International Workshop on Advanced Imaging Technology (IWAIT) 2024, 2024, Langkawi, Malaysia

Abstract

Graph theory is the main theory for handling data with complex relationships: social networks, protein structures, and web page links. In graph theory, an adjacency matrix represents a finite graph structure as a square matrix. Additionally, a graph embedding vector is a low-dimensional vector that extracts the features of a graph. However, adjacency matrices have a drawback of being sensitive to graph vertex ordering changes. In this study, we propose a neural network model using graph isomorphism problem to generate new graph embedding vectors. Experimental results showed that the embedding vectors are robust to vertex reordering.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yosuke Higuchi and Yoshimitsu Kuroki "Generation of graph embedding vectors based on graph isomorphism problem", Proc. SPIE 13164, International Workshop on Advanced Imaging Technology (IWAIT) 2024, 1316432 (2 May 2024); https://doi.org/10.1117/12.3019647

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