group_adj_perturb: Group adjacency matrices

In CommKern: Network-Based Communities and Kernel Machine Methods

Group adjacency matrices

Description

Description of the simulated group adjacency matrices function.

Usage

group_adj_perturb(group_network_list, n_nets, n_nodes)

Arguments

group_network_list	the output from group_network_perturb, which is a list of data frames detailing nodes, community assignments of each node, and edge weights between each dyad of nodes
n_nets	the number of networks simulated
n_nodes	the number of nodes in each simulated network (will be the same across all networks)

Details

This function takes the output from the group_network_perturb function, which is a list of data frames summarizing each simulated network, and creates an array of adjacency matrices. These adjacency matrices can then be used as input to any community detection algorithm (such as the hierarchical multimodal spinglass algorithm, hms).

Value

an array of adjacency matrices of dimension (n_nets x n_nodes x n_nodes)

See Also

group_network_perturb, hms

Examples

# Example 1
sim_nofuzzy <-
  group_network_perturb(
    n_nodes = 45,
    n_comm = 3,
    n_nets = 3,
    perturb_prop = 0.1,
    wcr = c(8, 8),
    bcr = c(1.5, 8)
  )

nofuzzy_adj <-
  group_adj_perturb(sim_nofuzzy, n_nets = 3, n_nodes = 45)

if (require(pheatmap)) {
  pheatmap::pheatmap(
    nofuzzy_adj[1,,],
    treeheight_row = FALSE,
    treeheight_col = FALSE
  )
}

# Example 2
sim_fuzzy <-
  group_network_perturb(
    n_nodes = 45,
    n_comm = 3,
    n_nets = 3,
    perturb_prop = 0.1,
    wcr = c(8, 8),
    bcr = c(1.5, 8),
    bfcr = c(3, 8),
    fuzzy_comms = c('comm_b','comm_c')
  )

fuzzy_adj <-
  group_adj_perturb(sim_fuzzy, n_nets = 3, n_nodes = 45)

if (require(pheatmap)) {
  pheatmap::pheatmap(
    fuzzy_adj[2,,],
    treeheight_row = FALSE,
    treeheight_col = FALSE
  )
}

CommKern documentation built on Sept. 23, 2022, 5:07 p.m.