generate_BA | R Documentation |
This function generates networks from the generalized Barabási-Albert model. In this model, the preferential attachment function is power-law, i.e. A_k = k^\alpha
, and node fitnesses are all equal to 1
. It is a wrapper of the more powerful function generate_net
.
generate_BA(N = 1000,
num_seed = 2 ,
multiple_node = 1 ,
m = 1 ,
alpha = 1)
N |
Integer. Total number of nodes in the network (including the nodes in the seed graph). Default value is |
num_seed |
Integer. The number of nodes of the seed graph (the initial state of the network). The seed graph is a cycle. Default value is |
multiple_node |
Positive integer. The number of new nodes at each time-step. Default value is |
m |
Positive integer. The number of edges of each new node. Default value is |
alpha |
Numeric. This is the attachment exponent in the attachment function |
The output is a PAFit_net
object, which is a List contains the following four fields:
graph |
a three-column matrix, where each row contains information of one edge, in the form of |
type |
a string indicates whether the network is |
PA |
a numeric vector contains the true PA function. |
fitness |
fitness values of nodes in the network. The fitnesses are all equal to |
Thong Pham thongphamthe@gmail.com
1. Albert, R. & Barabási, A. (1999). Emergence of scaling in random networks. Science, 286,509–512 (https://www.science.org/doi/10.1126/science.286.5439.509).
For subsequent estimation procedures, see get_statistics
.
For other functions to generate networks, see generate_net
, generate_ER
, generate_BB
and generate_fit_only
.
library("PAFit")
# generate a network from the BA model with alpha = 1, N = 100, m = 1
net <- generate_BA(N = 100)
str(net)
plot(net)
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