Note I've made another video called "Collect, Process, Visualize - Programming Social Graphs (Instagram, Python, Gephi)" that you might find interesting ua-cam.com/video/CgHDt_aKyCc/v-deo.html.
The modularity on this video is crazy high - that is, the content and production quality is massively higher than the view and subscriber count! Something very interesting is going on here.
Finally, it's out there ;) Read the description to find many useful further resources. By the way, I make music too, check it out here: ua-cam.com/users/splines or on Spotify, Tidal etc. (search for "Splines")
This video is so great that I learned so much more than reading PPTs of lecture slides. Plus how things progress logically makes it so enjoyable. I wish more people can find this video.
Thank you so much for your efforts. I was struggling with understanding this topic for more than two weeks and thanks to you I can finally understand a lot these concepts clearly
Yes, indeed. If the underlying data is directed, you get better results by using a slightly altered modularity formula for directed graphs. From what we've derived in the video, it's not far to get there. But oftentimes, there's no directionality data available, in which case we would use the exact formula discussed in this video.
Note I've made another video called "Collect, Process, Visualize - Programming Social Graphs (Instagram, Python, Gephi)" that you might find interesting ua-cam.com/video/CgHDt_aKyCc/v-deo.html.
most underrated videos, considering the immense effort put into creating such exceptional content!
The modularity on this video is crazy high - that is, the content and production quality is massively higher than the view and subscriber count! Something very interesting is going on here.
Finally, it's out there ;) Read the description to find many useful further resources.
By the way, I make music too, check it out here: ua-cam.com/users/splines or on Spotify, Tidal etc. (search for "Splines")
Incredible video! It's obvious much effort was put into it. The explanations are perfect.
Thank you so much , glad you liked the video.
This video is so great that I learned so much more than reading PPTs of lecture slides. Plus how things progress logically makes it so enjoyable. I wish more people can find this video.
Thank you for these kind words ;)
incredible video explaing Louvain Algorithm!
And thats the kind of explanation I was looking for
Thank you so much for your efforts. I was struggling with understanding this topic for more than two weeks and thanks to you I can finally understand a lot these concepts clearly
Thanks, glad it was helpful to you.
What an amazing video! Please dont stop
Thank you so much! I won't stop for sure, I just take a long time to produce new videos since I'm a full-time student ;) Stay tuned...
Very well-produced video.
Thanks a lot ;)
Some may think that this video deserves more views, but i disagree, every like given here equals to 1000 likes in an average video, amazing work!!!!
Amazing content, thank you so much for this!!
Hey, thanks a lot, glad it was helpful for you.
Great job.
Love the vid
I think it’s pretty important to consider directionality if the data is available
Yes, indeed. If the underlying data is directed, you get better results by using a slightly altered modularity formula for directed graphs. From what we've derived in the video, it's not far to get there. But oftentimes, there's no directionality data available, in which case we would use the exact formula discussed in this video.