Great video and am enjoying the GNN playlist. If there are open requests for new topics will it be okay to request for a hands on tutorial for skeletal action recognition (ex: berkley MHAD dataset) using temporal-based graph networks. Thanks
Dear instructor, is it possible to do node classification on temporal GNNs as well? for example, using a trained model predicting the category of each node during each time step based on given time series of node features?
My CSV dataset has time values and corresponding sensor values measured every 5 min, when i tried to make a graph out of this TS ,taking node the time value and sensor value its attribute. Node gets wrong sensor values. How shall I convert this CSV to correct Graph? Any basic codes, I am a beginner?
Thank you very much for your video!!! I look forward to the next part. I am currently working with elliptic dataset to create temporal based classification of a dynamic graph with pytorch geometric, but I am finding it impossible to adapt the dataset to pytorch geometric. I would be very grateful if you could show me how to create a custom temporal dynamic graph in the library because the toy examples don't help much. Regards.
Hi! I somehow didn't receive a notification for your comment but found it by accident :D Next part will be uploaded in the next days. I will say some words around datasets and hope this will help you with your issues. Otherwise feel free to leave a comment and I'll come back to you :) I agree the examples could be better :D
Thank you! This channel seems like it's run by someone well-read. I very much love that you include research papers too.
Thank you for the excellent presentation of the topic, a job well done!
Great first video on TGNNs! Looking forward to the next part
This work is crazy good!!
Amazing video on such a challenging topic
awesome explanation. Do share some more pytorch based example use-cases on temporal GNNs. Appreciate your sharing!
Hi! I have one for traffic forecasting :) and a video on how to generate temporal graph datasets
Great video and am enjoying the GNN playlist. If there are open requests for new topics will it be okay to request for a hands on tutorial for skeletal action recognition (ex: berkley MHAD dataset) using temporal-based graph networks. Thanks
Dear instructor, is it possible to do node classification on temporal GNNs as well? for example, using a trained model predicting the category of each node during each time step based on given time series of node features?
This video is wonderful! Please, I wish to know what is the difference between higher and lower proximity orders? (In the table shown at 11:55)
Hi, can node classification be done on these final temporal GNN outputs, for a predicted timestep in the future for each node?
My CSV dataset has time values and corresponding sensor values measured every 5 min, when i tried to make a graph out of this TS ,taking node the time value and sensor value its attribute. Node gets wrong sensor values. How shall I convert this CSV to correct Graph? Any basic codes, I am a beginner?
I don't understand the gating mechanism and the math behind the time series predictions. Any recommendations (i.e. papers)?
Great content, very helpful
Thank you so much
Happy that you found it useful :)
Nice work!
Thanks, really nice intro.
Thx!
how do we handle the case where graph changes over a period of time? I mean the graph at t1 will not be same as graph at t2.
This is called dynamic graph and for example explained in this paper: arxiv.org/abs/2006.10637
The important part is to only update nodes that are present in the current graph. But nodes might be added or removed over time
@@DeepFindr thanks for the paper. Your videos are really helpful and simple to understand.
can we use dgl for the GNN -lstm framework ?
Thank you for the very helpful video! Is the mentioned Google Colab notebook available to test and play with.
Very soon :) comes with the next video in the next days! Sorry for the delay
@@DeepFindr Thank you! :)
No problem at all! :) Thank you for sharing information and knowledge to the technical community
Amazing explanation sir!
Thank you so much!!!
Thank you very much for your video!!! I look forward to the next part. I am currently working with elliptic dataset to create temporal based classification of a dynamic graph with pytorch geometric, but I am finding it impossible to adapt the dataset to pytorch geometric. I would be very grateful if you could show me how to create a custom temporal dynamic graph in the library because the toy examples don't help much. Regards.
Hi! I somehow didn't receive a notification for your comment but found it by accident :D
Next part will be uploaded in the next days. I will say some words around datasets and hope this will help you with your issues. Otherwise feel free to leave a comment and I'll come back to you :)
I agree the examples could be better :D
Great content. Thank you.
great tutorial on TGNNs
Thanks, Very helpful!
very nicely explained..
Awesome animations! Which software did you use to create them?
Thank you