A simple way to learn metrics between attributed graphs

Yacouba Kaloga, Pierre Borgnat, Amaury Habrard

Keywords: Graph, Metric Learning, Classification

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 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.

Type Of Submission: Full paper proceedings track submission (max 9 main pages).

Type Of Submission: Full paper proceedings track submission.

Software: https://github.com/Yacnnn/SGML