random_module_structure: Create a random network structure for a module

In SeqNet: Generate RNA-Seq Data from Gene-Gene Association Networks

Description

A single, connected graph is created. The graph is initialized as a ring lattice, and edges are randomly rewired and/or removed. The procedure is similar to the Watts-Strogatz method, but the sampling of edges to modify can be based on the degree of each node.

Usage

random_module_structure(
  size,
  prob_rewire = 1,
  prob_remove = 0.5,
  weights = NULL,
  neig_size = 3,
  alpha = 100,
  beta = 1,
  epsilon = 10^-5,
  ...
)

Arguments

size	The number of nodes to include in the graph.
prob_rewire	The probability of rewiring an edge.
prob_remove	The probability of removing an edge.
weights	(Optional) Weights used for sampling nodes. See rewire_connections_to_node and remove_connections_to_node for details.
neig_size	The neighborhood size within which the nodes of the ring lattice are connected. The initial degree of each node is 2 * neig_size, so long as size >= (1 + 2 * neig_size).
alpha	A positive value used to parameterize the Beta distribution.
beta	A positive value used to parameterize the Beta distribution.
epsilon	A small constant added to the sampling probability of each node.
...	Additional arguments are ignored.

Value

An adjacency matrix representing the network structure.

Examples

# Create a random module structure (an adjacency matrix) for 10 nodes.
adj_mat <- random_module_structure(10)
# A network object can be created using this structure.
module <- create_module_from_adjacency_matrix(adj_mat)
nw <- create_network_from_modules(10, module)

SeqNet documentation built on July 9, 2021, 9:08 a.m.