steinernet 0.2.0

A SteinerTree library implementing several huristic and exact steiner methods.

SteinerNet

The Steiner Tree Approach refers to a method used in graph theory and network design to find the most efficient way to connect a set of points (nodes), potentially using extra intermediate points (called Steiner points) to minimize the total connection cost.

This the python eqiuivalent to SteinerNet in R. Released 10 years later on date the R version was released.

Installation

To install:

pip install steinernet

You can install the package locally using pip:

pip install .

Or, for development:

pip install -e .

Requirements

• Python 3.7+
• networkx

Usage

import networkx as nx
from steinernet import SteinerNet

# Create a sample graph
G = nx.cycle_graph(6)
terminals = [0, 2, 4]

# Initialize SteinerNet
sn = SteinerNet(G)

# Compute a Steiner tree using the random walk method
T = sn.steinertree(terminals,'ASP', repeats=1, optimize=True)

# Visualize the result
import matplotlib.pyplot as plt
nx.draw(T, with_labels=True)
plt.show()

Turorial

Check the Tutorial on tutorial file

Documentation

• SteinerNet(graph)

  • graph: networkx.Graph, the input undirected graph (optionally weighted)
  • Returns: SteinerNet object

• SteinerNet.random_walk_tree(terminals, seed=None)

  • terminals: list of node indices to connect
  • seed: (optional) random seed for reproducibility
  • Returns: networkx.Graph, a random graph

• SteinerNet.steinertree(terminals, method, repeats=1, optimize=True)

• terminals: list of node indices to connect

  • method: one of the methods SP, RSP, SPM, ASP, EXA (see their info in the Reference paper below)
  • • Returns: an approximate Steiner tree made by the selected method

Readme file for parralel running of the exact algorithm exists in README_PARALLEL

Reference

Afshin Sadeghi and Holger Froehlich, "Steiner tree methods for optimal sub-network identification: an empirical study", BMC Bioinformatics 2013 14:144, doi:10.1186/1471-2105-14-144

License

This project is open source under the Apache 2.0 License. You basically can use it in any commercial or research project.

Citation

To use this package in your work, cite this article as:

@article{sadeghi2013steiner,
  title={Steiner tree methods for optimal sub-network identification: an empirical study},
  author={Sadeghi, Afshin and Fr{\"o}hlich, Holger},
  journal={BMC bioinformatics},
  volume={14},
  pages={1--19},
  year={2013},
  publisher={Springer},
  doi = {https://doi.org/10.1186/1471-2105-14-144}
}

Link to R steinerNet repository.
Link to Python SteinerNet repository.

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