netdecom 0.0.4.5

Dimensionality Reduction and Decomposition of Undirected Graph Models and Bayesian Networks

NetDecom Documentation

Overview

NetDecom is a Python package for advanced graph analysis, providing algorithms for convex subgraph extraction and recursive decomposition of both undirected graphs and directed acyclic graphs (DAGs). Built on NetworkX, it offers efficient implementations of three core functionalities.

Installation

>>> pip install NetDecom

Core Functionalities

1. Convex Subgraph Extraction (Undirected Graphs)

Finds the minimal convex subgraph containing a given node set R:

>>> import NetDecom as nd
>>> import networkx as nx
>>> G = nx.Graph([(1, 2), (2, 3), (3, 4)])
>>> nd.IPA(G, [1, 3])  # Inducing Path Absorbing Algorithm
>>> nd.CMSA(G, [1, 3])  # Close Minimal Separator Absorbing Algorithm

2. Recursive Graph Decomposition

Decomposes graphs into maximal prime subgraphs using MCS ordering:

>>> MCS = [1, 2, 3, 4]  # Maximum Clique Sequence
>>> nd.Decom_CMSA((G, MCS))  # CMSA-based decomposition
>>> nd.Decom_IPA((G, MCS))  # IPA-based decomposition

3. DAG Convex Subgraph Extraction

For directed acyclic graphs:

>>> D = nx.DiGraph([(1, 2), (2, 3), (3, 4)])
>>> nd.CMDSA(D, [1, 3])  # Close Minimal D-Sepator Absorbing Algorithm

Notes

• All input graphs must be NetworkX Graph/DiGraph objects.
• MCS ordering should follow graph topology.
• DAG decomposition features are under development.