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PAFit (version 1.2.10)
Generative Mechanism Estimation in Temporal Complex Networks
Description
Statistical methods for estimating preferential attachment and node fitness generative mechanisms in temporal complex networks are provided. Thong Pham et al. (2015)
. Thong Pham et al. (2016)
. Thong Pham et al. (2020)
. Thong Pham et al. (2021)
.
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Install
install.packages('PAFit')
Monthly Downloads
586
Version
1.2.10
License
GPL-3
Issues
1
Pull Requests
0
Stars
17
Forks
1
Repository
https://github.com/thongphamthe/PAFit
Maintainer
Thong Pham
Last Published
March 28th, 2024
Functions in PAFit (1.2.10)
Search all functions
graph_to_file
Write the graph in a PAFit_net object to file
generate_fit_only
Simulating networks from the Caldarelli model
generate_simulated_data_from_estimated_model
Generating simulated data from a fitted model
joint_estimate
Joint inference of attachment function and node fitnesses
generate_ER
Simulating networks from the Erdos-Renyi model
get_statistics
Getting summarized statistics from input data
only_F_estimate
Estimating node fitnesses in isolation
graph_from_file
Read file to a PAFit_net object
only_A_estimate
Estimating the attachment function in isolation by PAFit method
generate_net
Simulating networks from preferential attachment and fitness mechanisms
plot.PAFit_result
Plotting the estimated attachment function and node fitness of a
PAFit_result
object
plot.PAFit_net
Plot a
PAFit_net
object
plot.Full_PAFit_result
Plotting the estimated attachment function and node fitness
print.Full_PAFit_result
printing information on the estimation result
print.CV_Data
Printing simple information of the cross-validation data
print.PAFit_net
Printing simple information of a
PAFit_net
object
print.CV_Result
Printing simple information of the cross-validation result
plot_contribution
Plotting contributions calculated from the observed data and contributions calculated from simulated data
print.PAFit_data
Printing simple information on the statistics of the network stored in a
PAFit_data
object
plot.PA_result
Plotting the estimated attachment function
print.PAFit_result
printing information on the estimation result stored in a
PAFit_result
object
summary.PA_result
Summary of the estimated attachment function
summary.CV_Data
Printing summary information of the cross-validation data
to_networkDynamic
Convert a PAFit_net object to a networkDynamic object
to_igraph
Convert a PAFit_net object to an igraph object
summary.CV_Result
Output summary information of the cross-validation result
print.PA_result
Printing information of the estimated attachment function
test_linear_PA
Fitting various distributions to a degree vector
summary.PAFit_data
Output summary information on the statistics of the network stored in a
PAFit_data
object
summary.Full_PAFit_result
Summary information on the estimation result
summary.PAFit_net
Summary information of a
PAFit_net
object
summary.PAFit_result
Output summary information on the estimation result stored in a
PAFit_result
object
Jeong
Jeong's method for estimating the preferential attachment function
Newman
Corrected Newman's method for estimating the preferential attachment function
from_igraph
Convert an igraph object to a PAFit_net object
Coauthorship network of scientists in the field of complex networks
A collaboration network between authors of papers in the field of complex networks with article time-stamps
generate_BA
Simulating networks from the generalized Barabasi-Albert model
generate_BB
Simulating networks from the Bianconi-Barabasi model
PAFit-package
Generative Mechanism Estimation in Temporal Complex Networks
as.PAFit_net
Converting an edgelist matrix to a PAFit_net object
PAFit_oneshot
Estimating the nonparametric preferential attachment function from one single snapshot.
from_networkDynamic
Convert a networkDynamic object to a PAFit_net object