Statistical Comparison among Brain Networks with Popular Network Measurement Algorithms

TL;DR

This study applies six popular network measurement algorithms to sixteen brain networks, analyzing correlations to understand their applicability and compare the networks statistically, complemented by visualizations and degree distribution histograms.

Contribution

It introduces a comprehensive statistical comparison of brain networks using multiple network measurement algorithms and explores their correlations and applicability.

Findings

Identified correlations among network measurement algorithms for brain networks

Provided visualizations and degree distributions for each brain network

Summarized statistical differences among sixteen brain networks

Abstract

In this research, a number of popular network measurement algorithms have been applied to several brain networks (based on applicability of algorithms) for finding out statistical correlation among these popular network measurements which will help scientists to understand these popular network measurement algorithms and their applicability to brain networks. By analysing the results of correlations among these network measurement algorithms, statistical comparison among selected brain networks has also been summarized. Besides that, to understand each brain network, the visualization of each brain network and each brain network degree distribution histogram have been extrapolated. Six network measurement algorithms have been chosen to apply time to time on sixteen brain networks based on applicability of these network measurement algorithms and the results of these network measurements are put into a correlation method to show the relationship among these six network measurement algorithms for each brain network. At the end, the results of the correlations have been summarized to show the statistical comparison among these sixteen brain networks.