Simulation data of the aging process in a network

Simulation data of the aging process in a network

About

This repository contains all the raw data related to the following paper which investigated aging behaviors of the scale free network with various parameters.

• R. Sekikawa and H. Watanabe, "Structural Changes and Percolation Transition in Networks after Aging Processes", J. Phys. Soc. Jpn. 94, 044004 (2025).
• https://doi.org/10.7566/JPSJ.94.044004
• arXiv:2412.20904

The source code for obtaining the data in this repository can be found at the following GitHub repository.

https://github.com/watanabe-appi/aging_network_cpp

How to generate figures

Just type make. Then you will have figures of the paper.

make

Extentions of files

• *.cfg Input files
• *.dat Data (output) file
• *.plt Plot file for Gnuplot
• *.py Python scripts

Input file

Here is a sample of an input file.

system_size = 10000
alpha = 0.0
beta = 0.0
n_sample = 1
data_dir = data
percolation_sample = 100
use_BA_model = False

The meaning of each parameter is as follows

Parameter	Value	Description
system_size	10000	Size of the system, e.g., number of nodes
alpha	0.0	Deleting parameter
beta	0.0	Adding parameter
n_sample	1	Number of samples (e.g., number of trials in an simulation)
data_dir	data	Directory for storing data
percolation_sample	100	Number of percolation samples
use_BA_model	False	Whether to use the Barabási-Albert (BA) model

Folder structures

├── LICENSE
├── Makefile
├── README.md
└── data
    ├── alpha_dep
    ├── ba_model
    ├── beta_dep
    ├── degree_average
    ├── degree_variance
    ├── finite_size
    ├── fitness
    ├── percolation
    └── phase_diagram

degree_average

Data for Fig. 3 (a), which shows the time evolutions of average of degrees.

degree_variance

Data for Fig. 3 (b), which shows the time evolutions of variance of degrees.

alpha_dep

Data for Figs 5(a), 5(b), which show the dependence of the degree distribution on the deleting parameter $\alpha$.

To create a complementary cumulative distribution function (ccdf file) from a degree distribution (dat file), use ccdf.py as follows

$ python3 ../ccdf.py degree_distribution_N10000_a-10_b20.dat
degree_distribution_N10000_a-10_b20.dat -> degree_distribution_N10000_a-10_b20.ccdf

beta_dep

Data for Figs. 6(a), 6(b), which show the dependence of the degree distribution on the adding parameter $\beta$. The ccdf file is created in the same way as the $\alpha$ dependencies.

percolation

Data for Figs. 7(a), 7(b), which show the dependence of the percolation probability on the parameters $\alpha$ and $\beta$.

phase_diagram

Data for Fig. 8 which show the phase diagram. The python script phasediagram.py generates the data for phase diagram from degree distributions (degree_distribution_*.dat), percolation probabilities (percolation*.dat), and time evolutions of degree variances (degree_variance_*.dat).

The script `phasediagram.py" will generates the following files.

• power_finite.dat
  • A set of parameters in which degree distribution obeys power-law and whose percolation probability is finite.
• power_zero.dat
  • A set of parameters in which degree distribution obeys power-law and whose percolation probability is zero.
• exp_finite.dat
  • A set of parameters in which degree distribution exhibits exponential decay and whose percolation probability is finite.
• exp_zero.dat
  • A set of parameters in which degree distribution exhibits exponential decay and whose percolation probability is zero.

Then the gnuplot file phasediagram.plt will generates Fig. 8.

fitness

Data for Figs. A.1 which show the time evolutions of the average fitness.

ba_model

Data for Figs. A.2, which show the aging behaviors of the model with fixing fitness to be $1$ (Barabási–Albert-like model).

finite_size

Data for Fig. B.1, which shows the finite-size effects on the percolation.

Contact

• hwatanabe__at__appi.keio.ac.jp (Corresponding Author)

Please replace __at__ by @.

License

CC-BY 4.0

We appreciate your citing the following reference when you publish the results using our data.

• R. Sekikawa and H. Watanabe, "Structural Changes and Percolation Transition in Networks after Aging Processes", J. Phys. Soc. Jpn. 94, 044004 (2025).
• https://doi.org/10.7566/JPSJ.94.044004