https://ericmjl.github.io/Network-Analysis-Made-Simple/

An introduction to network analysis and applied graph theory using Python and NetworkX
https://ericmjl.github.io/Network-Analysis-Made-Simple/

graph graph-theory live-tutorial network-analysis networkx networkx-graph networkx2 python tutorial

Last synced: about 2 months ago
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An introduction to network analysis and applied graph theory using Python and NetworkX

• Host: GitHub
• URL: https://ericmjl.github.io/Network-Analysis-Made-Simple/
• Owner: ericmjl
• License: mit
• Created: 2014-12-26T22:56:03.000Z (almost 10 years ago)
• Default Branch: master
• Last Pushed: 2024-09-23T18:12:22.000Z (about 2 months ago)
• Last Synced: 2024-09-25T01:16:32.626Z (about 2 months ago)
• Topics: graph, graph-theory, live-tutorial, network-analysis, networkx, networkx-graph, networkx2, python, tutorial
• Language: Jupyter Notebook
• Homepage: https://ericmjl.github.io/Network-Analysis-Made-Simple/index.html
• Size: 299 MB
• Stars: 1,033
• Watchers: 44
• Forks: 402
• Open Issues: 16
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

• awesome_network_science - Network Analysis Made Simple

README

        
# Network Analysis Made Simple

[![Build Status](https://travis-ci.org/ericmjl/Network-Analysis-Made-Simple.svg?branch=master)](https://travis-ci.org/ericmjl/Network-Analysis-Made-Simple)

Welcome to the GitHub repository for Network Analysis Made Simple!

This is a tutorial designed to teach you

the basic and practical aspects of graph theory.

It has been presented at multiple conferences (PyCon, SciPy, PyData, and ODSC)

in a variety of formats (ranging from 1.5 hr to 4 hour long workshops).

The material is designed for a live tutorial presentation,

with the code available for you to reference afterwards.

## Getting Started

Head over to [the official website][nams]!

[nams]: https://ericmjl.github.io/Network-Analysis-Made-Simple

## Support the project!

If you enjoy the material, please consider doing one of the following:

1. Share it around on Twitter!

2. Purchase a copy of the [LeanPub eBook](https://leanpub.com/nams)

3. Share it with your colleagues.