Fediverse as of 2018-08-24.
7 788 instances with 1 303 382 connections.
Let's look at the different clusters. They aren't clearly separated, but still visible.
Searchable (ctrl+f) SVG without lines to keep the filesize reasonable: https://lucahammer.at/vis/fediverse/2018-08-24-fediverse-min.svg
If you compare it with the screenshots, keep in mind that it is mirrored.
Beware of over interpretation. This is no scientific work. It's just one way to visualize instances. There are many others and probably better ones.
Qualifying the connections by amount of follow connections could draw a different image.
In the end it's not important for your everyday use. You can follow every account of every instance (as long as your instance doesn't cut ties with the other one).
Most important are the people you follow and what you toot.
I tried to clean up the script, but it's still messy. Feedback welcome: https://gist.github.com/lucahammer/eb68fc43a34877a304e7b81e5f729c4b
Data as GDF from yesterdays visualization (zipped, 10MB): https://lucahammer.at/vis/fediverse/2018-08-24-fediverse-GDF.zip
@Luca People, there's work to do! We're in the middle of the "niche instances" with a "non-technical focus"... 😟
@hiemstra It's easily possible that my quick interpretations are wrong. Niche is a rather broad term. And I would say it's a good thing.
@Luca I think it's pretty accurate (but we're supposed to be a technical university, so...)
Nice work, BTW!
@Luca How did you differentiate between the violet cluster and the others? I can't believe all the instances in the orange cluster are tech focused for example.
@Luca Cool. Now can anyone tell us what they're each about? It's really hard to find an instance that matches individual interests!
@darwinwoodka I didn't discover topical clusters and I believe it doesn't matter that much which instance you choose. I believe trust into the admins is more important than the theme.
@Luca have you ever ran a plot of instance age (if you save that data) vs nb connections (maybe relative nb connections)? curious to see how they‘re related
@Luca Oh I misunderstood, heh. Wonder why we got pushed to the right a bit. Might be cause I sometimes speak French.
@scarlett The SVG is searchable (Ctrl+f). https://lucahammer.at/vis/fediverse/2018-08-24-fediverse-min.svg
@Luca wow, I can actually see my self-run single-user instance from here! I feel, quite literally, extremely seen. 😁
@fuzzface @Cdespinosa I created a SVG that can be search through (ctrl+f): https://lucahammer.at/vis/fediverse/2018-08-24-fediverse-min.svg (Sorry for flipped axis)
@maloki Thanks for reminding me.
The images show names of instances that are connected through lines. They are one big hairball with two slightly visible groups of instances.
I admit I'm not too familiar with Gephi, but your description sounds like a "regular" force directed graph. If that already produces this, I'm curious what tSNE or umap would produce!
I bet even just feeding a 7788x7788 matrix with binary values based on whether they are connected or not would work well, it's kind of crazy how much structure those two algorithms can reveal!
@vanderZwan Which tools / libraries would you recommend? Preferably Python.
I will clean up my code and share the collection script as well as the collected data. Maybe you can use it.
Well, there is only one real umap implementation at the moment, by Leland McInnes, who the guy who developed the algorithm. Luckily its in Python :)
Here he gives a talk explaining how it works in accessible terms at SciPy 2018:
Wow looks interesting, can You toot me an URL, that I can see it later on a bigger screen. On my 5 inch Xperia can't see so much ;)
I like this🤔
.. thank you so kindly for posting❤️
Is there a time lapse so we can see the ebb. flow, migration, segregation, absorption, etc.. over the past 6 months, year, etc..?
Asking for a friend😏
@IncenseBerner I only have the data of those two dates, so there wouldn't be more to see than those two images.
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