Many real applications require the representation of complex entities and their relations. Frequently, networks are the chosen data structures, due to their ability to highlight topological and qualitative characteristics. In this work, we are interested in supervised classification models for data in the form of networks. Given two or more classes whose members are networks, we build mathematical models to classify them, based on various graph distances. Due to the complexity of the models, made of tens of thousands of nodes and edges, we focus on model simplification solutions to reduce execution times, still maintaining high accuracy. Experimental results on three datasets of biological interest show the achieved performance improvements.

中文翻译：

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代谢网络监督分类的模型简化

许多实际应用需要复杂实体及其关系的表示。通常，网络是选择的数据结构，因为它们能够突出拓扑和定性特征。在这项工作中，我们对网络形式的数据的监督分类模型感兴趣。给定两个或多个成员为网络的类，我们建立数学模型来根据不同的图距离对它们进行分类。由于模型的复杂性，由数以万计的节点和边组成，我们专注于模型简化解决方案以减少执行时间，同时仍保持高精度。在三个具有生物学意义的数据集上的实验结果显示了实现的性能改进。