Simple Message Passing on Graphs

Great video! Very clear.

I enjoyed the video. Thank you! Would be awesome to mention example applications of message passing.

Great video, but that t-shirt tho!
edit: seriously, thanks for the animations as well!

Why wouldn't message passing work without scaling with respect to the destination, and without the square root? i.e. just by using the initial formulation(D * A) with self-loop edges in A (and hence accounted in D), why can't I do the message passing?

Thanks! :) Enjoyed the video. Well explained and great examples.

great explanation . Thanks

Loving your channel. Too few likes for such a good video.

Is it possible to create a adjacent matrix from an image of a molecule or from just coordinates without knowng which components are connected ?. Thanks in advance.

Great explanation!

Great video!! I am currently working on an algorithm that builds an adjacency matrix from features extracted on a convolutional layer for a set of images (deep features). However, the formula just states that A is softmax(F)@softmax(F).T. This leaves me with an adjacency matrix with real numbers. Is this valid for message passing algorithms? I could imagine that it is some sort of “weighted edges”? Thanks!

Thanks so much for your class; I am a Ph.D. student, and your channel is helping me — way to go and Salute from Brazil.

Even though I’ve almost finished my master’s, the generalization of simple scalar arithmetic such as multiplication and division to vectors still surprise me. For example going from a/d to the inv(D)*A formula. Which seems even harder to comprehend if D wouldnt be diagonal.

Can anyone help me/point to any respurces that explain(not define) these fundamental matrix operations?

I couldn't understand why you changed degree of each node (1,2,3,1,1) to (1,2,3,4,5). Maybe I missed some point but I am curious to answer.

great video, thank you so much

It’s a great video, one thing (if I understood correctly) is to take different values for nodes features vector instead of nodes initial range positions. I didn’t catch that from the first time

Excellent! Thanks very much. That's outstandingly clear.
The resulting system implements diffusion with conservation of total, um, node stuff.

Wonderful video! Pity that it looks like you have stopped making them. If that were part of a Udemy course, I'd definitely take it.

Brilliant