NetworkX Crash Course - Graph Theory in Python

In this video, we learn about NetworkX, which is the primary Python library for working with graphs and networks.

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Timestamps
(0:00) Intro
(0:21) Fundamentals
(7:45) Adjacency Matrices
(11:50) Visualizing Graphs
(14:28) Complete Graphs
(16:22) Degree of Nodes
(19:14) Shortest Path
(20:40) Centrality
(27:52) Density & Diameter
(29:25) Eulerian Path
(31:00) Cliques
(32:25) Bridges
(36:15) Connected Components
(38:15) Outro

[thiagoporto]

It seems like whenever i'm trying to learn something new in python this man already has the exact tutorial i'm looking for. This channel is an invaluable resource.

[rabibasukala]

bro why you so underrated 😢. thank you for the video

[giorgegi]

This semester in my university they taught us about graphs but we implemented them in C programming language. I was curious how would they be implemented in python then this video pops up. Very interesting and very useful.

[geoafrikana]

This is wonderful. I would have asked for a little more graphics to explain concepts but you already said you assume familiarity with graph theory.

Once again, thanks for this highly useful tutorial.

[machinimaaquinix]

Perfect timing! I need to do some graph work in March! Excellent video.

[multitaskprueba]

Fantastic video! One of the best that I have seen in my life! Wonderful the idea of a crash course!!!

[RomualdMenuet]

Great tuto, thanks 😀
One small issue, you're inverting source and destination in all your adjacency matrix examples for directed graphs. Having 1 at (i, j) position (row i, col j) actually means the edge is from i to j while you're saying the opposite in your examples.

[svjatoslavblokhin]

Marvellous, bro! Many thanks for explaining this complicated material in easy way

[dushannagahawatta]

Thank you! This was a really informative session.

[peterdekam]

Your content is brilliant and brilliantly explained... Just enough and just in time! Is there a way to establish a triangular distribution of an edge weight instead of just using a "crisp" number?

[dlrmfemilianolako]

Your content is truly awe-inspiring! Your creativity and attention to detail really shines through in every video. Keep up the amazing work! 🔥🎥💯"

[ranjeetprasad]

How you know everything you are a real coding goat

[CarlosOPonce]

Thanks! It's a great video. I would have liked you to explain weighted graphs and some algorithms such as Dijkstra's or Bellman-Ford's, or Floyd-Warshall's. I also suggest you try trees and their corresponding algorithms. Maybe next one!

[konarkmadan]

'Networks' by Mark Newman. For those who want to study the theory in depth.

[jrichalot]

Thanks. A very efficient and clear summary. It helped me a lot in my studies.

[okoriechinonso]

Rich and well detailed tutorial on networkx. You type very fast, how do I develop myself to writing codes as quick as you do?

[wg]

ahh man this would have been awesome if knew it when i was taking GT class, awesome video, thanks as always.

[santoshabraham]

Excellent content, well explained. Thank you

[michelledahlberg-halaut]

I'm glad I'm not the only one that Google's how to pronounce words,

[RedCloudServices]

Can you make a video with the best visual libraries for NetworkX besides matplotlib? and which of those libraries allow search and filtering of a networkx graph