https://github.com/blargian/bgl

Repository with code for learning how to use the Boost Graph Library
https://github.com/blargian/bgl

bgl boost-graph boost-graph-library contributions-welcome example-code examples graph graphs helpwanted learning-boost learning-exercise opensource-projects

Last synced: 3 months ago
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Repository with code for learning how to use the Boost Graph Library

• Host: GitHub
• URL: https://github.com/blargian/bgl
• Owner: Blargian
• Created: 2023-12-08T15:01:36.000Z (over 1 year ago)
• Default Branch: master
• Last Pushed: 2023-12-12T19:14:54.000Z (over 1 year ago)
• Last Synced: 2025-02-03T10:46:32.716Z (5 months ago)
• Topics: bgl, boost-graph, boost-graph-library, contributions-welcome, example-code, examples, graph, graphs, helpwanted, learning-boost, learning-exercise, opensource-projects
• Language: C++
• Homepage:
• Size: 32.2 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Learning Boost Graph Library 💡

This is an opensource project with the aim of creating a repository with useful notes and code examples for learning the Boost Graph Library. BGL is an incredible piece of C++ design, which strives for genericity. Unfortunately this genericity can sometimes make it feel a little challenging to pick up. This project is aimed at lowering the barrier to entry for new comers to BGL. [Contributions](/doc/setup_instructions.md) are welcome, let's learn together!   

  

    File

    Description

  

  

    kevin-bacon.cpp

    An example which uses breadth-first search to find the bacon number of actors. (six degrees of Kevin Bacon problem) taken from the BGL book.

  

  

    kruskal.cpp

    A basic example of how to use Kruskals algorithm to find the MST. Taken from BGL library examples.

  

  

    tiny-ewg.cpp

    Reads in data from a textfile, creates a graph out of it and then finds the minimum spanning tree using Kruskal's algorithm

  

  

    tiny-ewg-improved.cpp

    Example by user sehe on stackexchange which makes some simplifications and improvements to the tiny-ewg example

  

Instructions to get the project working on your local machine can be found [here](/doc/setup_instructions.md)