shapG 0.14.2

Scalable Shapley Value Computation for Graph Data with Modular Architecture

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 (New Modular API)

import networkx as nx
from shapG import ShapGExplainer, GraphBuilder, FeatureImportanceVisualizer

# Build a graph from tabular data
builder = GraphBuilder()
G = builder.from_correlation(data, threshold=0.3)

# Compute approximate Shapley values
explainer = ShapGExplainer(depth=1, n_samples=15)
shapley_values = explainer.fit_explain(G)

# Visualize the results
viz = FeatureImportanceVisualizer()
viz.plot_importance(shapley_values)

Advanced Usage

Custom Characteristic Function

You can define a custom characteristic function:

from shapG import ShapGExplainer, CustomFunction

def my_coalition_function(coalition, graph):
    subgraph = graph.subgraph(coalition)
    return nx.density(subgraph) * len(coalition)

custom_func = CustomFunction(my_coalition_function)
explainer = ShapGExplainer(characteristic_function=custom_func)
shapley_values = explainer.fit_explain(G)

Customizing the Plot

from shapG import FeatureImportanceVisualizer

viz = FeatureImportanceVisualizer()
fig, ax = viz.plot_importance(
    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=False             # Show plot
)

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

Legacy API (Deprecated)

The legacy procedural APIs (shapG, shapley_value, graph_generator, plot) are deprecated and planned for removal in v0.15.0. Please migrate to the modular API (ShapGExplainer, ExactExplainer, GraphBuilder, FeatureImportanceVisualizer).

Performance Notes

Exact Shapley computation scales as O(2^n). For graphs with more than ~20 nodes, prefer approximate explainers such as ShapGExplainer, QRCSExplainer, or BlockQRCSExplainer.

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},
   }