| Title: | Network Metrics Based on Random Walks |
|---|---|
| Description: | There are two new network metrics, RWC (random walk centrality) and CBET (counting betweenness). Also available are the normalized versions of those metrics. These measures of centrality and betweenness are particularly useful for the analysis of very dense weighted networks which include loops. Traditional measures do not work as well for those network characteristics. The main reference is DePaolis at al (2022) <doi:10.1007/s41109-022-00519-2>. |
| Authors: | Fernando DePaolis [aut, cre] |
| Maintainer: | Fernando DePaolis <[email protected]> |
| License: | GPL-3 |
| Version: | 0.1.0 |
| Built: | 2025-02-14 03:56:02 UTC |
| Source: | https://github.com/fdepaolis/xtranat |
Counting Betweenness implemented as in DePaolis et al(2022)
cbet(A)cbet(A)
A |
The adjacency matrix of the network to be analyzed. It must be square. |
The vector containing the values of Counting Betweenness of the network..
cbet(exmpl_matrix)cbet(exmpl_matrix)
A normalized version of Counting Betweenness implemented as in DePaolis et al(2022)
cbet_norm(A)cbet_norm(A)
A |
The adjacency matrix of the network to be analyzed.It must be square. |
The vector containing the normalized values (between 0 and 1) of Counting Betweenness of the network.
cbet_norm(exmpl_matrix)cbet_norm(exmpl_matrix)
Contains a randomly created adjacency matrix
exmpl_matrixexmpl_matrix
A 10 by 10 square matrix
It is a 10 by 10 matrix with some values in the diagonal to represent loops
Created by the author as an example
data(exmpl_matrix)data(exmpl_matrix)
mean first-passage time implemented as in DePaolis et al(2022)
mfpt(A)mfpt(A)
A |
The adjacency matrix of the network to be analyzed |
mfpt.
Random Walk Centrality implemented as in DePaolis et al(2022)
rwc(A)rwc(A)
A |
The adjacency matrix of the network to be analyzed.It must be square. |
The vector containing the values of Random Walk Centrality of the network.
rwc(exmpl_matrix)rwc(exmpl_matrix)
A normalized version of Random Walk Centrality implemented as in DePaolis et al(2022)
rwc_norm(A)rwc_norm(A)
A |
The adjacency matrix of the network to be analyzed.It must be square. |
The vector containing the normalized values (between 0 and 1) of Random Walk Centrality of the network.
rwc_norm(exmpl_matrix)rwc_norm(exmpl_matrix)