Running Experiments

The library provides several experimental scripts for comprehensive hypernetwork analysis. Each script performs specific experiments and saves results in dedicated output directories.

Resilience Experiments

Test hypernetwork resilience by removing nodes using different strategies.

Command:

python examples/run_resilience_experiments.py [optional_dataset_path]

What it does:

  • Removes nodes at percentages: 1%, 2%, 5%, 10%, 25%

  • Uses three strategies: Random, TOPSIS Top (critical), TOPSIS Bottom (peripheral)

  • Computes metrics: connectivity, redundancy coefficient, s-walk efficiency

Results location: resilience_results/

  • resilience_analysis.png - Metric degradation plots

  • impact_comparison.png - Strategy comparison

  • resilience_summary.csv - Detailed numeric results

Perturbation Analysis

Analyze single and multiple perturbations with targeted attacks.

Command:

python examples/run_perturbation_analysis.py

What it does:

  • Single perturbations: 1%, 2%, 5%, 10% node removal

  • Multiple perturbations: Attack sequences with k ∈ {2, 5, 10, 25, 50, 100}

  • Compares Random vs TOPSIS targeting strategies

  • Analyzes component fragmentation and largest component evolution

Results location: results/

  • {dataset}_single_comparison.png - Single perturbation plots

  • {dataset}_multiple_timeline.png - Evolution over attack sequences

  • {dataset}_component_analysis.png - Fragmentation analysis

  • {dataset}_largest_components.png - Component metrics

  • perturbation_results_{timestamp}.json - Complete experimental data

  • analysis_summary.json - Executive summary

Comprehensive Node and Hyperedge Experiments

Run experiments on both node and hyperedge removal.

Command:

python examples/run_node_hyperedge_experiments.py

What it does:

  • Tests removal of both nodes and hyperedges

  • Computes traditional metrics (connectivity, redundancy)

  • Computes higher-order cohesion metrics (HOCR_m, LHC_m)

  • Removal percentages: 1%, 2%, 5%, 10%, 25%

Results location: resilience_results/plots/

  • node_removal_traditional_metrics.png - Node removal analysis

  • hyperedge_removal_traditional_metrics.png - Hyperedge removal analysis

  • higher_order_cohesion_comparison.png - Advanced metrics

  • strategy_effectiveness_heatmap.png - Comparative effectiveness

MCDM Methods Comparison

Compare different Multi-Criteria Decision Making methods for node selection.

Command:

python examples/compare_selection_methods.py [dataset_name]
# Or combine all results:
python examples/compare_selection_methods.py --combine

What it does:

  • Compares three MCDM methods: TOPSIS, WSM (Weighted Sum Model), MOORA

  • Tests targeted node removal using each method

  • Removal percentages: 5%, 10%, 25%

  • Analyzes whether simpler methods (WSM, MOORA) achieve comparable results to TOPSIS

Results location: comparison_results/

  • methods_comparison_all_datasets.png - Side-by-side comparison of all three methods

  • results_<dataset>.json - Results for individual datasets

  • comparison_results_all_datasets.json - Complete numerical results

  • <dataset>_comparison.png - Individual dataset plots

Statistical Analysis

Perform cross-domain statistical analysis on hypernetwork features.

Command:

python examples/run_statistical_analysis.py

What it does:

  • Computes structural features for all datasets in data/ directory

  • Performs ANOVA/Kruskal-Wallis tests across hypergraph families

  • Correlation analysis between features and resilience metrics

  • Normalized metrics for size-independent comparisons

Results location: statistical_analysis_results/

  • data_directory_features_by_family.png - Feature distributions by family

  • structural_correlations_heatmap.png - Feature correlation matrix

  • structural_features_scatter.png - Relationship visualizations

  • significant_correlations.png - Feature-resilience correlations

  • statistical_analysis_summary.csv - Complete statistical results

  • data_directory_features.csv - Computed features for all datasets

Working with Custom Datasets

To run experiments on your own hypernetwork data:

  1. Prepare your dataset

    Create a text file where each line represents a hyperedge with space-separated node IDs:

    1 2 3
2 3 4 5
3 4 6

  2. Place in data directory

    Save the file in the data/ folder

  3. Run experiments

    Execute any of the experiment scripts above

  4. View results

    Check the corresponding results directories for plots and data files

Example Workflow

Complete analysis workflow for a new dataset:

# 1. Run perturbation analysis
python examples/run_perturbation_analysis.py

# 2. Run resilience experiments
python examples/run_resilience_experiments.py data/your_dataset.txt

# 3. Run comprehensive analysis
python examples/run_node_hyperedge_experiments.py

# 4. Perform statistical analysis
python examples/run_statistical_analysis.py

Results will be organized in:

  • results/ - Perturbation analysis outputs

  • resilience_results/ - Resilience experiment outputs

  • statistical_analysis_results/ - Statistical analysis outputs