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  "path": "/post/49566880",
  "publishedAt": "2026-03-27T13:08:35.000Z",
  "site": "https://mander.xyz",
  "tags": [
    "Data is Beautiful",
    "podbrushkin",
    "dataisbeautiful",
    "2 comments",
    "tilvids.com",
    "share.tube",
    "conf.tube",
    "video.lqdn.fr",
    "skeptikon.fr",
    "spectra.video",
    "video.monsieurbidouille.fr",
    "aperi.tube",
    "tube.aquilenet.fr",
    "video.hardlimit.com",
    "www.kaggle.com/…/fediverse-graph-dataset-reduced"
  ],
  "textContent": "submitted by podbrushkin to dataisbeautiful\n24 points | 2 comments\n\n\nThis is a graph of Peertube instances following each other. There are 942 nodes and 10067 edges.\n\n> 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.\n\nColor 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.\n\nWhat 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.\n\nDoes 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.\n\nHere are top 10 instances and their centrality:\n\n  * tilvids.com 1.0\n  * share.tube 0.97\n  * conf.tube 0.90\n  * video.lqdn.fr 0.88\n  * skeptikon.fr 0.86\n  * spectra.video 0.74\n  * video.monsieurbidouille.fr 0.73\n  * aperi.tube 0.71\n  * tube.aquilenet.fr 0.71\n  * video.hardlimit.com 0.68\n\n\n\n## How to repeat this graph visualization\n\n  1. Download latest Peertube `instances.csv` and `interactions.csv` files here: www.kaggle.com/…/fediverse-graph-dataset-reduced\n  2. Import them to Gephi;\n  3. Apply `Giant Component` filter to remove nodes which are not connected to biggest network;\n  4. Apply `ForceAtlas 2` layout;\n  5. Run `Eigenvector centrality` Statistics (directed). It will add a new column to nodes table;\n  6. Apply `Nodes - Color - Ranking - Eigenvector centrality`;\n  7. Apply `Nodes - Size - Ranking - Eigenvector centrality`;\n  8. Configure Preview and export.\n\n\n\nP.S. On colorful image used as thumbnail of this post nodes are colored by Modularity (community detection).",
  "title": "Graph of Peertube - Centrality"
}