In defence of the simple: Euclidean distance for comparing complex networks

Autor:	Martínez, Johann H., Chavez, Mario
Rok vydání:	2018
Předmět:	Physics - Physics and Society Computer Science - Social and Information Networks Physics - Data Analysis Statistics and Probability 68R10 90C35 94C15
Druh dokumentu:	Working Paper
Popis:	To improve our understanding of connected systems, different tools derived from statistics, signal processing, information theory and statistical physics have been developed in the last decade. Here, we will focus on the graph comparison problem. Although different estimates exist to quantify how different two networks are, an appropriate metric has not been proposed. Within this framework we compare the performances of different networks distances (a topological descriptor and a kernel-based approach) with the simple Euclidean metric. We define the performance of metrics as the efficiency of distinguish two network's groups and the computing time. We evaluate these frameworks on synthetic and real-world networks (functional connectomes from Alzheimer patients and healthy subjects), and we show that the Euclidean distance is the one that efficiently captures networks differences in comparison to other proposals. We conclude that the operational use of complicated methods can be justified only by showing that they out-perform well-understood traditional statistics, such as Euclidean metrics. Comment: 4 pages, 3 figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1804.07533 Zobrazit plný text záznamu View this record from Arxiv