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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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Version

Install

install.packages('PAFit')

Monthly Downloads

462

Version

1.2.10

License

GPL-3

Issues

1

Pull Requests

0

Stars

17

Forks

1

Maintainer

Thong Pham

Last Published

March 28th, 2024

Functions in PAFit (1.2.10)

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