# Network graphs and leveraging NetworkX Python library

**URL:** <https://gramps.discourse.group/t/network-graphs-and-leveraging-networkx-python-library/6025>\
**Category:** Ideas\
**Created:** [September 8, 2024, 3:03pm UTC](https://gramps.discourse.group/t/network-graphs-and-leveraging-networkx-python-library/6025 "2024-09-08T15:03:07Z")\
**Posts on this page:** 1\
**Page:** 1

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**Author:** ![emyoulation](https://yyz2.discourse-cdn.com/free1/user_avatar/gramps.discourse.group/emyoulation/32/67_2.png) [@emyoulation](https://gramps.discourse.group/u/emyoulation)\
**Post date:** [September 8, 2024, 3:03pm UTC](https://gramps.discourse.group/t/network-graphs-and-leveraging-networkx-python-library/6025/1 "2024-09-08T15:03:07Z")

</div>

In the the “[Collaborate on Optimizing a new Custom Rule](https://gramps.discourse.group/t/collaborate-on-optimizing-a-new-custom-rule/3063)” thread, @StoltHD suggests:

> [@Collaborate on Optimizing a new Custom Rule](https://gramps.discourse.group/t/collaborate-on-optimizing-a-new-custom-rule/3063/6):
>
> This is a type of problem that would have easily been resolved by a network graph that shows all Gramps objects as nodes and all relations between gramps objects as edges and a then used a “show nearest neighbor” with 2 hops algorithm.
> 
> It’s not for nothing that I say network graphs would be perfect for genealogy research.
> 
> Maybe you can use [NetworkX](https://networkx.org/) and write a script to use in the Python Gramplet or SuperTool…

I prompted Perplexity AI to “assess the posting and expound upon what networkx is, and how it could be utilized to design a python module for use in Gramps”

> _ **Perplexity responds:** _  
> This posting suggests using network graphs, specifically with the NetworkX library in Python, to represent and analyze genealogical data in Gramps. Let’s break this down and explore the concept:
> 
> ### NetworkX:
> 
> NetworkX is a powerful Python library for working with complex networks and graph structures. (Under the Open source [3-clause BSD license](https://raw.githubusercontent.com/networkx/networkx/master/LICENSE.txt)) It provides tools for creating, manipulating, and studying the structure, dynamics, and functions of complex networks. Key features include:
> 
> 1. Support for various types of graphs (directed, undirected, multi-graphs)
> 2. A wide range of graph algorithms (shortest paths, centrality measures, community detection, etc.)
> 3. Network analysis and visualization capabilities
> 4. Integration with other scientific Python libraries (NumPy, SciPy, matplotlib)
> 
> ### Utilizing NetworkX for a Gramps module:
> 
> 1. Data representation:
> 
> > - Nodes: Represent Gramps objects (individuals, families, events, places, etc.)
> > - Edges: Represent relationships between objects (parent-child, spouse, event participation, etc.)
> 
> 1. Module functionality:
> 
> > - Create a graph from Gramps database
> > - Implement genealogy-specific algorithms (e.g., finding common ancestors, identifying pedigree collapse)
> > - Visualize family trees and relationships
> > - Perform network analysis on genealogical data
> 
> 1. Integration with Gramps:
> 
> > - Develop as a Python Gramplet or script for the SuperTool plugin
> > - Provide an interface for users to interact with the graph structure
> > - Offer visualization options within the Gramps UI
> 
> 1. Potential features:
> 
> > - “Show nearest neighbor” with n-hops: Identify related individuals within a specified number of relationship steps
> > - Identify potential missing links or inconsistencies in the family tree
> > - Calculate relationship paths between individuals
> > - Detect and visualize pedigree collapse or intermarriage patterns
> 
> 1. Benefits:
> 
> > - Improved data exploration and analysis capabilities
> > - Enhanced visualization of complex family relationships
> > - Potential for discovering new insights or connections in genealogical data
> 
> By leveraging NetworkX’s capabilities, a Gramps module could provide powerful tools for genealogists to analyze and visualize their family history data in new and insightful ways. The graph-based approach would allow for more complex queries and analyses that might be difficult or impossible with traditional database queries alone.

From [Water Programming: A Collaborative Research Blog](https://waterprogramming.wordpress.com/); [January 19, 2021](https://waterprogramming.wordpress.com/2021/01/19/basic-network-analysis-on-a-directed-network-using-networkx/) by [Antonia Hadjimichael](https://waterprogramming.wordpress.com/author/antoniahad/)

 ![image](https://global.discourse-cdn.com/free1/uploads/gramps/original/2X/f/fd4e7c0df2ebb56c8ff35a0a0a806c6fcb00ee00.jpeg)