shapG 0.13.3

A library to compute Shapley values in graphs.

ShapG: a fast and exactly approach to approximate the Shaple value on graph

Installation

pip install shapG

Features

• Exact Shapley value computation for small to medium-sized graphs
• Fast approximate Shapley value computation for large graphs using local search
• Visualization tools for Shapley values
• Utility functions for graph generation and analysis

Quick Start

import networkx as nx
from shapG.shapley import shapG, graph_generator
from shapG.plot import plot

# Generate a random graph
G = graph_generator(n_nodes=10, density=0.5)

# Compute approximate Shapley values
shapley_values = shapG(G, depth=1, m=15)

# Visualize the results
plot(shapley_values, top_n=10)

Advanced Usage

Custom Characteristic Function

You can define a custom characteristic function:

def my_coalition_function(G, S):
    # Your custom characteristic function
    subgraph = G.subgraph(S)
    return nx.density(subgraph) * len(S)

# Use your custom function
shapley_values = shapG(G, f=my_coalition_function)

More example, see ./examples.

Customizing the Plot

fig, ax = plot(
    shapley_values,
    top_n=5,                    # Show only top 5 values
    style='seaborn-v0_8',       # Matplotlib style
    file_name="shapley.eps",    # Save to file
    title="Node Importance",    # Custom title
    figsize=(10, 6),            # Figure size (width, height)
    color="#2E86C1",            # Bar color
    show_values=True,           # Show values next to bars
    value_format="{:.4f}",      # Format for displayed values
    show_plot: bool = False      # Show plot
)

# Further customize the plot using matplotlib objects
ax.set_xlabel("Contribution Score", fontsize=14)
plt.show()

License

MIT License

Citation

If you find this code useful in your research, please consider citing:

1. For centralities measures:

   @article{zhao2024centralitymeasuresopiniondynamics,
         title={Centrality measures and opinion dynamics in two-layer networks with replica nodes}, 
         author={Chi Zhao and Elena Parilina},
         year={2024},
         eprint={2406.18780},
         archivePrefix={arXiv},
         primaryClass={physics.soc-ph},
         journal={arXiv preprint arXiv:2406.18780},
         url={https://arxiv.org/abs/2406.18780}, 
   }
   

2. For the new method for explanable ai:

   @article{ZHAO2025110409,
         title = {ShapG: New feature importance method based on the Shapley value},
         journal = {Engineering Applications of Artificial Intelligence},
         volume = {148},
         pages = {110409},
         year = {2025},
         issn = {0952-1976},
         doi = {https://doi.org/10.1016/j.engappai.2025.110409},
         url = {https://www.sciencedirect.com/science/article/pii/S0952197625004099},
         author = {Chi Zhao and Jing Liu and Elena Parilina},
   }