Great lecture! Investigation of the combination of GNN and SVM may be promising in future research. Moreover, employing the technique to reinforce learning should also be interesting as it can introduce explainability to the black-box model.
Love the content - especially the clarity and simple summary! Can you do a tutorial of a project using causal GNN framework? Can the GNN model first identify causal relationship? Then secondly, using that to create link between the node. Finally using GNN to predict an attribute of the nodes?
If you can include comparison with other simpler causal inference model that will be great! (Model performance, explainability and training complexity)
Thanks for the feedback and the suggestion! I add it to the list, but can't promise when I get to do it. Usually I set-up polls and let the majority decide :)
Hi! Thanks :) Have you seen this Github summary? github.com/FanzhenLiu/Awesome-Deep-Community-Detection Besides that the next video is about unsupervised GNNs, which are also part of the methods used for community detection. Hope this will be benefitial :)
I wonder if there is anything like more mainstream linear causality models like IV regression, difference in difference or regression discontinuity design with machine learning methods to incorporate nonlinearity in high dimensions.
Good question :) I've not come across anything like that. But I'm pretty sure there are interesting ideas that can be applied in the machine learning domain.
Hey, A quick question; would it be possible for you upload a video on crime forecasting using GNNs? There are practically no videos on UA-cam, we'll able to learn a lot especially about the MAPPING THE LOCATION DATA in a real world scenario! Datasets are already available online!
The background information on causality was succinct and well put. Thank you!
Great lecture! Investigation of the combination of GNN and SVM may be promising in future research. Moreover, employing the technique to reinforce learning should also be interesting as it can introduce explainability to the black-box model.
The contents you are creating are great!, keep them on
Thank you!
Love the content - especially the clarity and simple summary! Can you do a tutorial of a project using causal GNN framework? Can the GNN model first identify causal relationship? Then secondly, using that to create link between the node. Finally using GNN to predict an attribute of the nodes?
If you can include comparison with other simpler causal inference model that will be great! (Model performance, explainability and training complexity)
Thanks for the feedback and the suggestion! I add it to the list, but can't promise when I get to do it. Usually I set-up polls and let the majority decide :)
@@DeepFindr Really appreciate if you decide to do the same.
Causality can be biased in at least three ways: omitted variable, selection bias, reverse causality. Which one does GNN address?
Your Content is Super Amazing. I have a Kind Request. Can you please do a tutorial on Community Detection? That would be really helpful.
Hi!
Thanks :)
Have you seen this Github summary? github.com/FanzhenLiu/Awesome-Deep-Community-Detection
Besides that the next video is about unsupervised GNNs, which are also part of the methods used for community detection. Hope this will be benefitial :)
great vid
Is there any plan to publish a code tutorial?
Hi! Not in the near future but I will add it to the list, thanks!
I wonder if there is anything like more mainstream linear causality models like IV regression, difference in difference or regression discontinuity design with machine learning methods to incorporate nonlinearity in high dimensions.
Good question :) I've not come across anything like that. But I'm pretty sure there are interesting ideas that can be applied in the machine learning domain.
Maybe this paper: academic.oup.com/ectj/article/23/2/177/5722119
Hey, A quick question; would it be possible for you upload a video on crime forecasting using GNNs? There are practically no videos on UA-cam, we'll able to learn a lot especially about the MAPPING THE LOCATION DATA in a real world scenario! Datasets are already available online!
Thanks
Thank you!!