Next I want to explore the /api/v1/instance/peers endpoint.

Before I start with my own data collection, let's take a look at the data @Gargron published half a year ago ( and were clearly the most connected instances.

On average each instance was connected to 175 other ones.


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.

On the right side in turquoise is the predominantly Japanese cluster. About 35% of the instances.

On the left in orange is the English and European cluster. 43%.

I don't see any subclusters.

At the top in violet are several niche instances. I would say the only thing they have in common is the non-technical focus. 17%.

Finally in green a little cluster (4%) of instances with domains ending in Again mostly Japanese.

If you know more, please let me know.

Searchable (ctrl+f) SVG without lines to keep the filesize reasonable:

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.

@Luca This is brilliant work. Thank you very much for doing this.

@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!

@hiemstra Thanks.

Academia will probably always be niche.

@Luca woah, i can't believe cr.t is actually big enough to be properly seen in this screenshot tbh
it's cool how many instances there are that i've never even heard of in the violet area

@Luca Oh, Eldritch Café is actually quite visible on the violet cluster!

@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

@halcy I only have data from those two dates. I fear that would make the plot useless/misleading. Maybe someone else has more data. I think collected those numbers.

@halcy @Luca the graph would be more interesting if some API returned servers that people on that server actually followed - peers reveals only knowledge of existence.

@Luca Oh I misunderstood, heh. Wonder why we got pushed to the right a bit. Might be cause I sometimes speak French.

@Luca currently is there any way to browse this? I'd like to find my instance

@Luca wow, I can actually see my self-run single-user instance from here! I feel, quite literally, extremely seen. 😁

@Cdespinosa @Luca This looks amazing. Will there be a possibility at some point to drill down or find a particular instance?

@Luca 😮 This is a cool visualization. Liking this place more and more by the day.

@djsundog Only 5000x5000px at the moment:

Will look into rendering something bigger tomorrow.

@Luca Could you consider adding image descriptions to these posts?

@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.

Did you us umap or tSNE? Or something else entirely?

@vanderZwan with ForceAtlas2 for the positions and Modularity for cluster detection.

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.

@Luca That looks awesome! Is there a chance to get the graph data?

@Luca May I ask what is the green cluster at the right of the picture?

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