论文标题
一种简单的学习属性图之间指标的方法
A Simple Way to Learn Metrics Between Attributed Graphs
论文作者
论文摘要
对象之间的良好距离和相似性度量的选择对于许多机器学习方法很重要。因此，近年来已经开发了许多度量学习算法，主要用于欧几里得数据，以提高分类或聚类方法的性能。但是，由于难以在归因图之间建立可计算，高效和可区分的距离，尽管社区的兴趣很大，但很少开发适合图形的度量学习算法。在本文中，我们通过提出一个新的简单图表度学习 - SGML-模型，该模型很少，该模型很少，几乎可以基于简单的图形卷积神经网络-SGCN-和最佳传输理论元素。该模型使我们能够与标记（属性）图的数据库建立适当的距离，以提高简单分类算法（例如$ k $ -nn）的性能。可以快速训练这个距离，同时保持良好的性能，如本文所述的实验研究所示。
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.
