SDDE (version 1.0.0)

random_network: creates random augmented networks X and Y

Description

This function allows the user to create random network X and an associated augmented network Y using either the Erdos-Renyi model or the Barabasi-Albert model.

Usage

random_network(original_node, additional_node, ngroup, vertex_ratio, type)

Arguments

original_node

the number of nodes in the original network X (default=25)

additional_node

the number of additional node in network Y (default=5)

ngroup

the number of additional taxa groups in network Y (default=1)

vertex_ratio

the vertex to node ratio (default=between 1 and 5)

type

Either 'erdos' for the Erdos-Renyi model or 'barabasi' for the Barabasi-Albert model

Value

• Return a data.frame containing g1, g2, the total_nodes and the total_edges numbers of network Y and the total_original_nodes number of network X.

encoding

ISO-8859-2

Examples

## Create a small random_network
	random_network()
	## Expected result:
	#
	#  $g1
	#   IGRAPH UNW- 25 23 -- Erdos renyi (gnm) graph
	#   + attr: name (g/c), type (g/c), loops (g/l), m (g/n), name (v/c), tax
	#    (v/c), weight (e/n)
	#
	#  $g2
	#   IGRAPH UNW- 30 30 -- Erdos renyi (gnm) graph
	#   + attr: name (g/c), type (g/c), loops (g/l), m (g/n), name (v/c), tax
	#    (v/c), weight (e/n)
	#
	#   $total_nodes
	#	[1] 30
	#
	#	$total_edges
	# 	[1] 30
	#
	#   $total_original_nodes
	#   [1] 25
	#
	## Create two networks using the Erdos-Renyi model with 100 nodes in network X 
	## and 10 additional nodes in network Y of 3 types. 
	random_network(100,10,3);
	## Create a random networks of 20 +10 additional node using the  Barabsi-Albert model
	## and compute the corresponding SDDE path types.
	## l <- random_network(20,10,ngroup=1,vertex_ratio=1, type='barabasi');
	## complete_network(l$g1, l$g2);