PAFit_oneshot: Estimating the nonparametric preferential attachment function...

In thongphamthe/PAFit: Generative Mechanism Estimation in Temporal Complex Networks

Estimating the nonparametric preferential attachment function from one single snapshot.

Description

This function estimates the attachment function A_k from one snapshot.

Usage

PAFit_oneshot(net_object, 
              M    = 10,
              S    = 5,
              loop = 5,
              G    = 1000)

Arguments

net_object	an object of class PAFit_net that contains the network. Any time-step information, if available, will be ignored.
M	Integer. Number of simulated networks in each iteration. Default is 10.
S	Integer. Number of iterations inside each loop. Default is 5.
loop	Integer. Number of loops of the whole process. Default is 5.
G	Integer. Number of bins for the PA function. Default is 1000.

Value

Outputs a PAFit_result object.

Author(s)

Thong Pham [e-mail]

References

1. Pham, T., Sheridan, P. & Shimodaira, H. (2021). Non-parametric estimation of the preferential attachment function from one network snapshot. Journal of Complex Networks 9(5): cnab024. (\Sexpr[results=rd]{tools:::Rd_expr_doi("10.1093/comnet/cnab024")}).

Examples

## Not run: 
  library("PAFit")
  net_1    <- generate_BA(N = 10000, alpha = 1) # true attachment exponent = 1.0
  result_1 <- PAFit_oneshot(net_1)
  print(result_1)

  
  net_2    <- generate_BA(N = 10000, alpha = 0.5) # true attachment exponent = 0.5
  result_2 <- PAFit_oneshot(net_2)
  print(result_2)
  
## End(Not run)

thongphamthe/PAFit documentation built on March 30, 2024, 4:14 p.m.