These are the experiments for the paper "Robust Parameter Fitting to Realistic Network Models via Iterative Stochastic Approximation".

Additional data can be found at https://doi.org/10.5281/zenodo.10629451.

• Make sure you have Python, Pip and R installed.
• Checkout this repository
• Install the python dependencies with

pip3 install -r requirements.txt

• Install the pygirgs package at https://github.com/PFischbeck/pygirgs

• Install the R dependencies (used for plots) with

R -e 'install.packages(c("ggplot2", "reshape2", "plyr", "dplyr", "scales"), repos="https://cloud.r-project.org/")'

• Download the file konect-data.zip from Zenodo and extract its contents into the folder input_data/konect
• Optional: Download the file output-data.zip from Zenodo and extract its contents into the folder output_data. This way, you can access all experiment results without running them yourself.

• The folder input_data contains all networks used (KONECT) and generated (by random network models).
• The folder output_data contains all data and figures generated by the experiments.
• The files experiments-*.py are used for executing the Python experiments.
• The folder R contains R scripts for generating the plots.
• The folder plots contains all plots generated by the R scripts.

• Execute python3 experiments-models.py <experiment_name> to run experiments related to the models. The different experiments are as follows:
  • For some <model> in "erdos-renyi", "chung-lu-pl", "girg-1d", run:
    • sample_and_measure_<model> to sample from the model and measure the resulting feature values
    • fit_parameters_<model> to fit the parameters via the ParFit algorithm
    • fitted_sample_and_measure_<model> to sample and measure features based on the fitted parameters
  • merge_csv to merge all files for further processing in R
• Execute python3 experiments-konect.py <experiment_name> to run experiments related to the real-world networks. The different experiments are as follows:
  • clean_graphs to convert each real-world network to its largest component and convert the format
  • measure_target_features to measure the target features for every real-world network
  • For some <model> in "erdos-renyi", "chung-lu-pl", "girg-1d", run:
    • fit_parameters_<model> to fit the parameters via the ParFit algorithm
    • fitted_sample_and_measure_<model> to sample and measure features based on the fitted parameters
  • merge_csv to merge all files for further processing in R
• Execute python3 experiments-ablation.py <experiment_name> to run experiments related to the ParFit configuration (i.e., alpha and threshold values). The different experiments are as follows:
  • For some <model> in "erdos-renyi", "chung-lu-pl", "girg-1d", run:
    • fit_parameters_alpha_<model> to fit the parameters via the ParFit algorithm (for alpha experiments)
    • fit_parameters_threshold_<model> to fit the parameters via the ParFit algorithm (for treshold experiments)
    • fitted_sample_and_measure_alpha_<model> to sample and measure features based on the fitted parameters (for alpha experiments)
    • fitted_sample_and_measure_threshold_<model> to sample and measure features based on the fitted parameters (for threshold experiments)
  • merge_csv to merge all files for further processing in R

Run Rscript R/<scriptname> to run R scripts, found in the Rsubfolder. For example, run Rscript R/erdos-renyi-ablation-alpha.R to generate figures and tables related to the effect of the alpha configuration of ParFit for the ER model. The resulting figures and tables can be found in output_data/figures.