In Adjacency matrix (Weighted & Directed), the path between AF is A-C-E-F, can't we have path A-C-F? Also, the path for BC is X which means no connectivity, however as per the graph there is a connection which is B-A-C. Please correct me if my understanding is wrong.
For the directed path you have to check for the direction of the data which limits the path, however for an undirected graph you can create path more freely and you will have lot more paths to work with. Thanks for your comment and feedback, sincerely appreciate it.
Hi Sir, I would like to thank you very much for providing such beautiful content. I have one doubt in adjacency matrix for undirected and weighted graphs . For example if Node A and Node B are connected and having an edge weight 12(Assumption) Now in Adjacency matrix 1. How we will decide edge weight for (Ath row, Bth Column) or (Bth row, Ath Column) ?. 2. if we can assign for only one way (Ath_row, Bth_Column) How we can decide that ? please help me in understanding this. Thanks in Advance
Thanks for the comment and I do appreciate your kind feedback. The edge-weight is totally depends on what the graph represents. It can be any integer number which can be related to all the nodes in your graph and the weight can represent anything within the graph i.e. distance between nodes, the cumulative distance from a certain point, anything you would like your edge to represent holistically for every node.
I think it´s wrong in the video. In the undirected case the matrix must be symmetric. This means that the weight from A -> B must be also in the column from B -> A.
Hi Sir..I can't able to use Digraph code. It is showing as "Digraph attribute is not included in Networkx package. My version is 3.1. I tried to reinstall it, but again the same 3.1 version is installing. It is not upgrade to 3.2. Is there any other way to upgrade the Networkx package sir..
I'm sorry, but I think that the adjacency matrix shown at 25:40 is wrong. If the graph is undirected, as it's stated in the title, the matrix should be symmetric, i.e. identical to its transposed.
Thanks for your comment, appreciate it. The code and details about this video are located below: (as well as within the video details): github.com/prodramp/DeepWorks/tree/main/GraphNeuralNetworks
This is really a good lecture, I did wish in the beginning of the video, when you use the code. nx.draw(G) I wish you used, nx.draw(G, with_labels=True) As this would have helped beginners to better visualize what you are explaining, it includes the labels of the nodes in drawing. But this is really good, thank you.
The Part 2 of this GNN series is out here: th-cam.com/video/VDzrvhgyxsU/w-d-xo.html
Thank you so much for your hard work and great explanations! That is exactly what GNN newbies need!
You're very welcome! Appreciate your comment and hope keep doing the same.
Thank you for all the efforts you put into this! It's really good to follow along - will go for part 2 now :)
Thanks and appreciate your feedback.
This is exactly what I was looking for. Thanks a lot!!!!
Thank you very much for this great tutorial!
Waiting part 2 ...
Appreciate it kindly... Part 2 is on its way, giving it the final touch before uploading.
Part 2 was released last week and here is the url - th-cam.com/video/VDzrvhgyxsU/w-d-xo.html
This is amazing!
Thank you so much, appreciate your comment.
Thank u so much
Thanks
In Adjacency matrix (Weighted & Directed), the path between AF is A-C-E-F, can't we have path A-C-F? Also, the path for BC is X which means no connectivity, however as per the graph there is a connection which is B-A-C.
Please correct me if my understanding is wrong.
For the directed path you have to check for the direction of the data which limits the path, however for an undirected graph you can create path more freely and you will have lot more paths to work with.
Thanks for your comment and feedback, sincerely appreciate it.
AF -> A-C-F, no need to include E.
BC -> B-A-C, it's ur graph, didn't u see?
Hi Sir, I would like to thank you very much for providing such beautiful content.
I have one doubt in adjacency matrix for undirected and weighted graphs . For example if Node A and Node B are connected and having an edge weight 12(Assumption) Now in Adjacency matrix
1. How we will decide edge weight for (Ath row, Bth Column) or (Bth row, Ath Column) ?.
2. if we can assign for only one way (Ath_row, Bth_Column) How we can decide that ?
please help me in understanding this. Thanks in Advance
Thanks for the comment and I do appreciate your kind feedback.
The edge-weight is totally depends on what the graph represents. It can be any integer number which can be related to all the nodes in your graph and the weight can represent anything within the graph i.e. distance between nodes, the cumulative distance from a certain point, anything you would like your edge to represent holistically for every node.
I think it´s wrong in the video. In the undirected case the matrix must be symmetric. This means that the weight from A -> B must be also in the column from B -> A.
core is a homogeneoud data set can we do this also on heterogeneous ?
Please peovide all the resources in the description 4.21
Hi Sir..I can't able to use Digraph code. It is showing as "Digraph attribute is not included in Networkx package. My version is 3.1. I tried to reinstall it, but again the same 3.1 version is installing. It is not upgrade to 3.2. Is there any other way to upgrade the Networkx package sir..
Audio only works in left headphone. Can't hear anything in the right one. This is the case for both of your GNN videos.
Yes, I am sorry as I had an issue with my editing due to mic system. The newer videos do not show this problem.
use mono mode instead of using stereo mode to fix your problem
@@TzStories Tysm. The tutorial is just wat I need but I could not stand the audio coming from the side. :)
I'm sorry, but I think that the adjacency matrix shown at 25:40 is wrong. If the graph is undirected, as it's stated in the title, the matrix should be symmetric, i.e. identical to its transposed.
Thanks for your comment, appreciate it sincerely.
I will take a look and update as necessary.
@@650AILab Thanks to you for your very useful videos!
You are right. The graph should be directed based on the adjacency matrix.
respected sir, is GNN is applicable for image classification?
For that you gotta represent image as graph. Do you know how to represent it?
your video can be played in only one side of headphone
Sir can u give source code? Or not
Thanks for your comment, appreciate it.
The code and details about this video are located below: (as well as within the video details):
github.com/prodramp/DeepWorks/tree/main/GraphNeuralNetworks
59:12
This is really a good lecture, I did wish in the beginning of the video, when you use the code.
nx.draw(G)
I wish you used, nx.draw(G, with_labels=True)
As this would have helped beginners to better visualize what you are explaining, it includes the labels of the nodes in drawing. But this is really good, thank you.
Great suggestion! Thanks for your comment.