A simple way to learn metrics between attributed graphs
- URL: http://arxiv.org/abs/2209.12727v1
- Date: Mon, 26 Sep 2022 14:32:38 GMT
- Title: A simple way to learn metrics between attributed graphs
- Authors: Yacouba Kaloga and Pierre Borgnat and Amaury Habrard
- Abstract summary: We propose a new Simple Graph Metric Learning - SGML - model with few trainable parameters.
This model allows us to build an appropriate distance from a database of labeled (attributed) graphs to improve the performance of simple classification algorithms.
- Score: 11.207372645301094
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The choice of good distances and similarity measures between objects is
important for many machine learning methods. Therefore, many metric learning
algorithms have been developed in recent years, mainly for Euclidean data in
order to improve performance of classification or clustering methods. However,
due to difficulties in establishing computable, efficient and differentiable
distances between attributed graphs, few metric learning algorithms adapted to
graphs have been developed despite the strong interest of the community. In
this paper, we address this issue by proposing a new Simple Graph Metric
Learning - SGML - model with few trainable parameters based on Simple Graph
Convolutional Neural Networks - SGCN - and elements of Optimal Transport
theory. This model allows us to build an appropriate distance from a database
of labeled (attributed) graphs to improve the performance of simple
classification algorithms such as $k$-NN. This distance can be quickly trained
while maintaining good performances as illustrated by the experimental study
presented in this paper.
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