2021
DOI: 10.1007/s10994-021-06009-3
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Distance metric learning for graph structured data

Abstract: Graphs are versatile tools for representing structured data. As a result, a variety of machine learning methods have been studied for graph data analysis. Although many such learning methods depend on the measurement of differences between input graphs, defining an appropriate distance metric for graphs remains a controversial issue. Hence, we propose a supervised distance metric learning method for the graph classification problem. Our method, named interpretable graph metric learning (IGML), learns discrimin… Show more

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Cited by 9 publications
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