Graphs in Python - Theory and Implementation

Graphs in Python - Theory and Implementation

David Landup
Dimitrije Stamenic
Jovana Ninkovic


Graphs are an extremely versatile data structure. More so than most people realize! Graphs can be used to model practically anything, given their nature of modeling relationships and hierarchies.

Nature and human creations are very hierarchical.

Words in a sentence, and sentences in a book can be graphs - represented as a grid. Pixels in an image can be a graph as well. A graph can represent a sequence of steps you want to take, or a sequence of operations an algorithm runs. A graph can represent a molecule, their arrangements, up to humans and their relationships, up to streets and intersections, up to cities, countries, planets!

From an atomic scale to a universe scale - graphs can be used to model any sort of relationship or hierarchy.

Many common problems in the world can be represented in graphs. Where to open up restaurants to cover an area with guaranteed delivery times, with the minimum investment? Where to route a driver to avoid congestion and get to a target place as soon as possible? How to organize the flow of electrical energy in a city's grids? There isn't a shortage of problems that are inherently graph-based.

Getting started with graphs can be daunting. Literature can be scattered and searching online typically involves various different implementations and a non-continuity between the material you're reading.

For that reason, we're compiling a free introductory course to Graphs in Python, in an attempt to standardize the fundamentals to help you form a solid basis on graph theory.


Last Updated: Apr 2022

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