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Lecture Notes in Computer Science
Evaluating similarity between graphs is of major importance in several computer vision and pattern recognition problems, where graph representations are often used to model objects or interactions between elements. The choice of a distance or similarity metric is, however, not trivial and can be highly dependent on the application at hand. In this work, we propose a novel metric learning method to evaluate distance between graphs that leverages the power of convolutional neural networks, whiledoi:10.1007/978-3-319-66182-7_54 fatcat:ydvxdnwacjf5vdoa4dbnfavp7e