Description Usage Arguments Value Author(s) References See Also Examples
View source: R/only_A_estimate.R
This function estimates the attachment function A_k by PAFit method. The method has a hyper-parameter r. It first performs a cross-validation step to select the optimal parameter r for the regularization of A_k, then uses that r to estimate the attachment function with the full data.
1 2 3 4 5 6 7 | only_A_estimate(net_object ,
net_stat = get_statistics(net_object),
p = 0.75 ,
stop_cond = 10^-8 ,
mode_reg_A = 0 ,
MLE = FALSE ,
...)
|
net_object |
an object of class |
net_stat |
An object of class |
p |
Numeric. This is the ratio of the number of new edges in the learning data to that of the full data. The data is then divided into two parts: learning data and testing data based on |
stop_cond |
Numeric. The iterative algorithm stops when abs(h(ii) - h(ii + 1)) / (abs(h(ii)) + 1) < stop.cond where h(ii) is the value of the objective function at iteration ii. We recommend to choose |
mode_reg_A |
Binary. Indicates which regularization term is used for A_k:
|
MLE |
Logical. If |
... |
Outputs a Full_PAFit_result
object, which is a list containing the following fields:
cv_data
: a CV_Data
object which contains the cross-validation data. This is the final Normally the user does not need to pay attention to this data. NULL
if MLE = TRUE
.
cv_result
: a CV_Result
object which contains the cross-validation result. Normally the user does not need to pay attention to this data. NULL
if MLE = TRUE
.
estimate_result
: this is a PAFit_result
object which contains the estimated PA function and its confidence interval. It also includes the estimated attachment exponenent α (assuming the model A_k = k^α) in the field alpha
, and the confidence interval of α (in the field ci
) when possible. In particular, the important fields are:
ratio
: this is the selected value for the hyper-parameter r.
k
and A
: a degree vector and the estimated PA function.
var_A
: the estimated variance of A.
var_logA
: the estimated variance of log A.
upper_A
: the upper value of the interval of two standard deviations around A.
lower_A
: the lower value of the interval of two standard deviations around A.
center_k
and theta
: when we perform binning, these are the centers of the bins and the estimated PA values for those bins. theta
is similar to A
but with duplicated values removed.
var_bin
: the variance of theta
. Same as var_A
but with duplicated values removed.
upper_bin
: the upper value of the interval of two standard deviations around theta
. Same as upper_A
but with duplicated values removed.
lower_lower
: the lower value of the interval of two standard deviations around theta
. Same as lower_A
but with duplicated values removed.
g
: the number of bins used.
alpha
and ci
: alpha
is the estimated attachment exponenet α (when assume A_k = k^α), while ci
is the confidence interval.
loglinear_fit
: this is the fitting result when we estimate α.
objective_value
: values of the objective function over iterations in the final run with the full data.
diverge_zero
: logical value indicates whether the algorithm diverged in the final run with the full data.
Thong Pham thongphamthe@gmail.com
1. 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).
2. Pham, T., Sheridan, P. & Shimodaira, H. (2016). Joint Estimation of Preferential Attachment and Node Fitness in Growing Complex Networks. Scientific Reports 6, Article number: 32558. (doi: 10.1038/srep32558).
See get_statistics
for how to create summerized statistics needed in this function.
See Newman
and Jeong
for other methods to estimate the attachment function A_k in isolation.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 | ## Not run:
library("PAFit")
set.seed(1)
#### Example 1: Linear preferential attachment #########
# a network from BA model
net <- generate_net(N = 1000 , m = 50 , mode = 1, alpha = 1, s = 0)
net_stats <- get_statistics(net, only_PA = TRUE)
result <- only_A_estimate(net, net_stats)
# plot the estimated attachment function
plot(result, net_stats)
# true function
true_A <- result$estimate_result$center_k
lines(result$estimate_result$center_k, true_A, col = "red") # true line
legend("topleft" , legend = "True function" , col = "red" , lty = 1 , bty = "n")
#### Example 2: a non-log-linear preferential attachment #########
# A_k = alpha* log (max(k,1))^beta + 1, with alpha = 2, and beta = 2
set.seed(1)
net <- generate_net(N = 1000 , m = 50 , mode = 3, alpha = 2, beta = 2, s = 0)
net_stats <- get_statistics(net,only_PA = TRUE)
result <- only_A_estimate(net, net_stats)
# plot the estimated attachment function
plot(result, net_stats)
# true function
true_A <- 2 * log(pmax(result$estimate_result$center_k,1))^2 + 1 # true function
lines(result$estimate_result$center_k, true_A, col = "red") # true line
legend("topleft" , legend = "True function" , col = "red" , lty = 1 , bty = "n")
#############################################################################
#### Example 3: another non-log-linear preferential attachment kernel ############
set.seed(1)
# A_k = min(max(k,1),sat_at)^alpha, with alpha = 1, and sat_at = 200
# inverse variance of the distribution of node fitnesse = 10
net <- generate_net(N = 1000 , m = 50 , mode = 2, alpha = 1, sat_at = 200, s = 0)
net_stats <- get_statistics(net, only_PA = TRUE)
result <- only_A_estimate(net, net_stats)
# plot the estimated attachment function
true_A <- pmin(pmax(result$estimate_result$center_k,1),200)^1 # true function
plot(result , net_stats, max_A = max(true_A,result$estimate_result$theta))
lines(result$estimate_result$center_k, true_A, col = "red") # true line
legend("topleft" , legend = "True function" , col = "red" , lty = 1 , bty = "n")
## End(Not run)
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