% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/generate_category_graphs.R
\name{generate_category_graphs}
\alias{generate_category_graphs}
\title{Generate a Set of Graphs with Similar Community Structures for a Category}
\usage{
generate_category_graphs(
  n_graphs = 10,
  n_nodes = 100,
  n_communities = 4,
  community_sizes = NULL,
  base_intra_prob = 0.8,
  base_inter_prob = 0.2,
  intra_prob_variation = 0.05,
  inter_prob_variation = 0.05,
  seed = NULL
)
}
\arguments{
\item{n_graphs}{An integer specifying the number of graphs to generate. Default is 10.}

\item{n_nodes}{An integer specifying the total number of nodes (brain regions). Default is 100.}

\item{n_communities}{An integer specifying the number of communities. Default is 4.}

\item{community_sizes}{An integer vector specifying the sizes of each community.
If NULL, communities are of equal size. Default is NULL.}

\item{base_intra_prob}{A numeric value between 0 and 1, or a numeric vector
of length \code{n_communities}, specifying the base probability of an edge
existing between nodes within the same community. If a vector, each element
sets the base probability for the corresponding community. Default is 0.8.}

\item{base_inter_prob}{A numeric value between 0 and 1 specifying the base probability
of an edge existing between nodes from different communities. Default is 0.2.}

\item{intra_prob_variation}{A numeric value specifying the maximum variation to apply
to the intra-community probability for each graph. Default is 0.05.}

\item{inter_prob_variation}{A numeric value specifying the maximum variation to apply
to the inter-community probability for each graph. Default is 0.05.}

\item{seed}{An optional integer for setting the random seed to ensure reproducibility.
Default is NULL.}
}
\value{
A list of symmetric binary adjacency matrices with no self-loops, representing
  brain networks with similar community structures.
}
\description{
This function generates a list of adjacency matrices representing brain networks
belonging to the same category (e.g., Control group). The generated graphs have
similar community structures but vary slightly in their intra-community and
inter-community connection probabilities to reflect natural variability.
}
\examples{
# Generate a set of 5 graphs for the Control category
control_graphs <- generate_category_graphs(n_graphs = 5, n_nodes = 100, n_communities = 4,
                                           base_intra_prob = 0.8, base_inter_prob = 0.2)
}
