network-dismantling

network-dismantling is a library for network dismantling, which refers to the process of strategically removing nodes from a network to disrupt its functionality. This repository provides implementations of various network dismantling algorithms, leveraging the power of PyTorch for efficient computation and flexibility.

Installation

To install the dependencies, easily install from PyPI:

pip install network-dismantling

Make sure you have PyTorch installed if you want to run a neural-network model. You can install PyTorch from here.

Or directly using conda:

conda install network-dismantling

Alternatively, you can clone the repository and install it locally:

git clone https://github.com/njublockchain/network-dismantling.git
cd network-dismantling
pip install -e .

Usage

# Create a test graph
G = nx.karate_club_graph()

# Initialize CoreHD strategies
corehd_strategy = CoreHDDismantling()

# Create NetworkDismantlers with different strategies
corehd_dismantler = NetworkDismantler(corehd_strategy)

# Dismantle the graph using both strategies
num_nodes_to_remove = int(G.number_of_nodes() * 0.1)  # Remove 10% of nodes

dismantled_G_corehd, removed_nodes_corehd = corehd_dismantler.dismantle(G, num_nodes_to_remove)

print(f"Original graph: {G.number_of_nodes()} nodes, {G.number_of_edges()} edges")
print(f"CoreHD dismantled graph: {dismantled_G_corehd.number_of_nodes()} nodes, {dismantled_G_corehd.number_of_edges()} edges")
print(f"CoreHD removed nodes: {removed_nodes_corehd}")

G = nx.karate_club_graph()

# Initialize CoreGDM strategy
coregdm_strategy = CoreGDMDismantling()

# Create NetworkDismantler with CoreGDM strategy
coregdm_dismantler = NetworkDismantler(coregdm_strategy)

# Dismantle the graph using CoreGDM
num_nodes_to_remove = int(G.number_of_nodes() * 0.1)  # Remove 10% of nodes

dismantled_G_coregdm, removed_nodes_coregdm = coregdm_dismantler.dismantle(G, num_nodes_to_remove)

print(f"Original graph: {G.number_of_nodes()} nodes, {G.number_of_edges()} edges")
print(f"CoreGDM dismantled graph: {dismantled_G_coregdm.number_of_nodes()} nodes, {dismantled_G_coregdm.number_of_edges()} edges")
print(f"CoreGDM removed nodes: {removed_nodes_coregdm}")

    # Create a test graph
    G = nx.karate_club_graph()
    
    # Initialize different dismantling strategies
    dismantling_strategies = [
        CollectiveInfluenceDismantling(l=2),
        ExplosiveImmunizationDismantling(q=0.1, num_iterations=10),
        CoreGDMDismantling(),
        GNDDismantling(),
        CoreHDDismantling()
    ]
    
    # Initialize evaluation metrics
    metrics = [
        LCCSizeMetric(),
        NumComponentsMetric(),
        AvgPathLengthMetric(),
        GlobalEfficiencyMetric(),
        AvgClusteringMetric()
    ]
    
    # Choose evaluation strategy
    evaluation_strategy = RelativeChangeStrategy()
    
    # Create evaluator
    evaluator = DismantlingEvaluator(metrics, evaluation_strategy)
    
    # Compare strategies
    num_nodes_to_remove = int(G.number_of_nodes() * 0.1)  # Remove 10% of nodes
    comparison_results = DismantlingEvaluator.compare_strategies(G, dismantling_strategies, num_nodes_to_remove, evaluator)
    
    # Print detailed results
    for strategy_name, metrics in comparison_results.items():
        print(f"\n{strategy_name}:")
        for metric, value in metrics.items():
            print(f"  {metric}: {value:.4f}")

Documentation

The full documentation is available at network-dismantling.readthedocs.io.

Contributing

We welcome contributions to the network-dismantling library! If you have an idea for an improvement or a new feature, feel free to open an issue or submit a pull request. Please make sure to follow our contributing guidelines.