Detect graph communities for each biodiversity feature.

Usage

get_metrics(connect_mat, which_community = "s_core")

Arguments

connect_mat: a data.frame object where the edge lists are contained. See more in details.
which_community: character value for community type detection. It can be one of "s_core", "louvain", "walktrap", "eigen", "betw", "deg" or "page_rank". The default is "s_core".

Details

Function get_metrics is used to calculate graph metrics values. The edge lists created from the previous step, or inserted directly from the user are used in this step to create graphs. The directed graphs are transformed to undirected. The function is based on the igraph package which is used to create clusters using Louvain and Walktrap and calculate the following metrics: Eigenvector Centrality, Betweenness Centrality and Degree and PageRank. S-core is calculated using the package brainGraph.

connect_mat is either the output of preprocess_graphs or a custom edge list data.frame object, with the following columns:

feature: feature name.
from.X: longitude of the origin (source).
from.Y: latitude of the origin (source).
to.X: longitude of the destination (target).
to.Y: latitude of the destination (target).
weight: connection weight.

Value

A list containing input for basic_scenario or connectivity_scenario.

References

Csárdi, Gábor, and Tamás Nepusz. 2006. The Igraph Software Package for Complex Network Research. InterJournal Complex Systems: 1695. https://igraph.org.

Csárdi, Gábor, Tamás Nepusz, Vincent Traag, Szabolcs Horvát, Fabio Zanini, Daniel Noom, and Kirill Müller. 2024. igraph: Network Analysis and Visualization in R. doi:10.5281/zenodo.7682609 .

Watson, Christopher G. 2024. brainGraph: Graph Theory Analysis of Brain MRI Data. doi:10.32614/CRAN.package.brainGraph .

Examples

# Read connectivity files from folder and combine them
combined_edge_list <- preprocess_graphs(system.file("external",
                                        package="priorCON"),
                                        header = FALSE, sep =";")

# Set seed for reproducibility
set.seed(42)

# Detect graph communities using the s-core algorithm
pre_graphs <- get_metrics(combined_edge_list, which_community = "s_core")