| Type: | Package |
| Title: | Network Metrics Based on Random Walks |
| Version: | 0.1.0 |
| 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>. |
| License: | GPL-3 |
| Encoding: | UTF-8 |
| LazyData: | true |
| RoxygenNote: | 7.2.3 |
| Suggests: | knitr, rmarkdown, igraph, kableExtra, testthat (≥ 3.0.0) |
| Config/testthat/edition: | 3 |
| VignetteBuilder: | knitr |
| Depends: | R (≥ 2.10) |
| URL: | https://github.com/fdepaolis/xtranat |
| BugReports: | https://github.com/fdepaolis/xtranat/issues |
| NeedsCompilation: | no |
| Packaged: | 2023-03-23 14:57:30 UTC; fernandodepaolis |
| Author: | Fernando DePaolis 
[aut, cre] |
| Maintainer: | Fernando DePaolis <fdepaolis@middlebury.edu> |
| Repository: | CRAN |
| Date/Publication: | 2023-03-27 12:30:02 UTC |
Computes Counting Betweenness
Description
Counting Betweenness implemented as in DePaolis et al(2022)
Usage
cbet(A)
Arguments
A |
The adjacency matrix of the network to be analyzed. It must be square. |
Value
The vector containing the values of Counting Betweenness of the network..
Examples
cbet(exmpl_matrix)
Computes Counting Betweenness in normalized format
Description
A normalized version of Counting Betweenness implemented as in DePaolis et al(2022)
Usage
cbet_norm(A)
Arguments
A |
The adjacency matrix of the network to be analyzed.It must be square. |
Value
The vector containing the normalized values (between 0 and 1) of Counting Betweenness of the network.
Examples
cbet_norm(exmpl_matrix)
Data to showcase the functions in the xtranat package
Description
Contains a randomly created adjacency matrix
Usage
exmpl_matrix
Format
A 10 by 10 square matrix
Details
It is a 10 by 10 matrix with some values in the diagonal to represent loops
Source
Created by the author as an example
Examples
data(exmpl_matrix)
Computes mfpt
Description
mean first-passage time implemented as in DePaolis et al(2022)
Usage
mfpt(A)
Arguments
A |
The adjacency matrix of the network to be analyzed |
Value
mfpt.
Computes Random Walk Centrality
Description
Random Walk Centrality implemented as in DePaolis et al(2022)
Usage
rwc(A)
Arguments
A |
The adjacency matrix of the network to be analyzed.It must be square. |
Value
The vector containing the values of Random Walk Centrality of the network.
Examples
rwc(exmpl_matrix)
Computes Random Walk Centrality in normalized format
Description
A normalized version of Random Walk Centrality implemented as in DePaolis et al(2022)
Usage
rwc_norm(A)
Arguments
A |
The adjacency matrix of the network to be analyzed.It must be square. |
Value
The vector containing the normalized values (between 0 and 1) of Random Walk Centrality of the network.
Examples
rwc_norm(exmpl_matrix)