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R: A function to generate simulated networks from the...

GenerateNet

R Documentation

A function to generate simulated networks from the Barabasi-Albert model or the fitness model

Description

This function generates networks from the Barabasi-Albert model or the fitness model. The number of edges of the new node at each time-step can be specified to be fixed, or followed a Poisson distribution. In the latter case, the mean of the Poisson distribution is either held fixed or increased with time.

Usage

GenerateNet(N        = 1000, m      = 1    , mode     = c(1,2,3), alpha = 1, beta = 1, 
            sat_at   = 100 , offset = 1    , rate     = 0       , shape = 0,
            num_seed = 2   , prob_m = FALSE, increase = FALSE   , log   = FALSE)

Arguments

`N`	Integer. Total number of nodes in the network (including the nodes in the seed graph). Default value is 1000.
`m`	Integer. The number of edges of the new node. Default value is 1.
`mode`	Integer. Indicates the attachment function to be used in generating the network. If mode == 1, the attachment function is A_k = k^α. If mode == 2, the attachment function is A_k = min(k,sat_at)^α. If mode == 3, the attachment function is A_k = α log (k)^β. Default value is 1.
`alpha`	Numeric. If mode == 1, this is the attachment exponent in the attachment function A_k = k^α. If mode == 2, this is the attachment exponenet in the attachment function A_k = min(k,sat_at)^α. If mode == 3, this is the alpha in the attachment function A_k = α log (k)^β + 1.
`beta`	Numeric. This is the beta in the attachment function A_k = α log (k)^β + 1.
`sat_at`	Integer. This is the saturation position sat_at in the attachment function A_k = min(k,sat_at)^α.
`offset`	Numeric. The attachment value of degree 0. Default value is 1.
`rate`	Numeric. The rate parameter in the Gamma prior for node fitness. If either rate or shape is 0, node fitness f is fixed at 1 (i.e. Barabasi-Albert model)
`shape`	Numeric. The shape parameter in the Gamma prior for node fitness. If either rate or shape is 0, node fitness f is fixed at 1 (i.e. Barabasi-Albert model)
`num_seed`	Integer. The number of nodes of the seed graph (the initial state of the network). Default value is 2.
`prob_m`	Logical. Indicates whether we fix the number of edges of a new node as a constant, or let it follows a Poisson distribution. If prob_m == TRUE, the number of edges of a new node follows a Poisson distribution. The mean of this distribution depends on the value of increase and log. Default value is FALSE.
`increase`	Logical. Indicates whether we increase the mean of the Poisson distribution over time. If increase == FALSE, the mean is fixed at m. If increase == TRUE, the way the mean increases depends on the value of log. Default value is FALSE.
`log`	Logical. Indicates how to increase the mean of the Poisson distribution. If log == TRUE, the mean increases logarithmically with the number of current nodes. If log == FALSE, the mean increases linearly with the number of current nodes. Default value is FALSE.

Value

The output is a List contains the following two fields:

`graph`	a 3-column matrix, where each row contains information of one edge, in the form of (from_id, to_id, time_stamp). From_id is the id of the source, to_id is the id of the destination.
`fitness`	fitness values of nodes in the network

Author(s)

Thong Pham thongpham@thongpham.net

References

1. Pham, T. and Sheridan, P. and Shimodaira, H. (2015). Nonparametric estimation of the preferential attachment function in complex networks: evidence of deviations from log linearity, in press. Proceedings of ECCS 2014: European Conference on Complex Systems.

2. Pham T, Sheridan P, Shimodaira H (2015) PAFit: A Statistical Method for Measuring Preferential Attachment in Temporal Complex Networks. PLoS ONE 10(9): e0137796. doi:10.1371/journal.pone.0137796 (http://dx.doi.org/10.1371/journal.pone.0137796)

Examples

library("PAFit")
#Generate a network from the original BA model with alpha = 1, N = 100, m = 1
data   <- GenerateNet(N = 100,m = 1,mode = 1, alpha = 1, shape = 0, rate = 0)

Results


R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(PAFit)
> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/PAFit/GenerateNet.Rd_%03d_medium.png", width=480, height=480)
> ### Name: GenerateNet
> ### Title: A function to generate simulated networks from the
> ###   Barabasi-Albert model or the fitness model
> ### Aliases: GenerateNet
> ### Keywords: scale free BA network preferential attachment
> ###   Bianconi-Barabasi model fitness model
> 
> ### ** Examples
> 
> library("PAFit")
> #Generate a network from the original BA model with alpha = 1, N = 100, m = 1
> data   <- GenerateNet(N = 100,m = 1,mode = 1, alpha = 1, shape = 0, rate = 0)
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>