2018/04/20 by Johann H. Martínez, Mario Chávez, Martínez, Johann H. +1
Biochemistry, Genetics and Molecular Biology · Neuroscience · Physics and Astronomy · #68R10 #90C35 #94C15 #Bioinformatics and Genomic Networks #Complex Network Analysis Techniques #Data Analysis #FOS: Computer and information sciences #FOS: Physical sciences #Functional Brain Connectivity Studies #Physics and Society (physics.soc-ph) #Social and Information Networks (cs.SI) #Statistics and Probability (physics.data-an)
paper · pdf · doi:10.48550/arxiv.1804.07533
openalex publication_date 2018/04/20 · openalex created_date 2018/05/07 · openalex updated_date 2026/07/28
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.