This is a graph of Peertube instances following each other. There are 942 nodes and 10067 edges.
On Peertube, an instance X can follow an instance Y to let its users see all the videos posted on Y. This graph is a directed graph.
Color and size of nodes depends on how big their Eigenvector centrality is. Nodes which have 0 centrality are blue and small, nodes with bigger centrality are big and red.
What centrality represents? Instances which are not followed by anyone have 0 centrality. Instances (A) with a lot of followers (B) have bigger centrality. If those followers (B) themselves have followers ©, it means centrality of A will be even higher.
Does it mean anything in context of Peertube? I’m not sure. Considering chain of three instances: (A) <- (B) <- ©, when (A) posts a video, does it appear in ©? Probably not. But if it was so, then centrality would’ve mean this: Videos posted on instances with high centrality spread across entire network, while videos posted on instances with 0 centrality are not visible anywhere else.
Here are top 10 instances and their centrality:
- http://tilvids.com/ 1.0
- http://share.tube/ 0.97
- http://conf.tube/ 0.90
- http://video.lqdn.fr/ 0.88
- http://skeptikon.fr/ 0.86
- http://spectra.video/ 0.74
- http://video.monsieurbidouille.fr/ 0.73
- http://aperi.tube/ 0.71
- http://tube.aquilenet.fr/ 0.71
- http://video.hardlimit.com/ 0.68
How to repeat this graph visualization
- Download latest Peertube
instances.csvandinteractions.csvfiles here: https://www.kaggle.com/datasets/marcdamie/fediverse-graph-dataset-reduced - Import them to Gephi;
- Apply
Giant Componentfilter to remove nodes which are not connected to biggest network; - Apply
ForceAtlas 2layout; - Run
Eigenvector centralityStatistics (directed). It will add a new column to nodes table; - Apply
Nodes - Color - Ranking - Eigenvector centrality; - Apply
Nodes - Size - Ranking - Eigenvector centrality; - Configure Preview and export.
P.S. On colorful image used as thumbnail of this post nodes are colored by Modularity (community detection).
This is really cool. I feel like people tend to want to gather some sort of critical insight from these graph visualizations but I just enjoy taking in the shape of this data.
Any particular explanation for the topmost featured graph?
Not really, I don’t see no well-defined communities (except for those little green guys at the bottom). If there were real clusters, they would’ve been visible by mere placement of nodes, without the need for coloring them. Originally, it was communities which I hoped to discover. Well, to the left there are several groups of yellow nodes - when I was inspecting them closely, I’ve noticed they are grouped not because they’re connected to each other, but because they follow and they are followed by same set of nodes. That was the reason they “gravitated” to each other.