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The embedding is kind of weird. Like, there's no reason a "degree: 1" node should be so far away from its sibling. Example: https://imgur.com/a/7Cktyzp https:/
by tantalor 2y ago
The embedding is kind of weird. Like, there's no reason a "degree: 1" node should be so far away from its sibling.
Example: https://imgur.com/a/7Cktyzp https://imgur.com/a/7Cktyzp
This makes the graph look more random/noisy/disorganized than it actually is.
- theophilec 2y agoI agree the spatialization could be better. I used one of the algorithms in Gephi-lite directly. Do you have a favorite spatialization algorithm to recommend?
- theophilec 2y agoFYI I updated the visualization. Credit goes to @jacomyal from @ouestware on GitHub who helped me out.
- tantalor 2y agoThe "Chloé Woestelandt" test is still just as bad
- theophilec 2y agoI see this: https://imgur.com/a/SGnbXjG https://imgur.com/a/SGnbXjG
- tantalor 2y agoYes! This looks amazing. Thanks! I made a mistake; I had checked the other link in your post ("You can explore the visualization here: [Interactive Culinary Network]") instead of the main link.
- visarga 2y agoYeah, they should have used UMAP or tSNE to cluster the data a bit
- peppery 2y agoSince you did the hard work of parsing rich metadata already, it would be even cooler if your network visualization oriented nodes by some of this information. Here the 'hiveplot' idea (https://hiveplot.com/ https://hiveplot.com/ ) is often even more useful than e.g. springloaded or UMAP based layouts; clustering into semantically-meaningful categories into axes (say, city or arrondissement? years open? cuisine? an explicit phylogeny from oldest culinary grandparents to youngest?) then choosing a coordinate to localize nodes on the axes (total node degree? prix? "les plus" tags?...) automatically compels us think about salient features of the data.
- deleted 2y ago[deleted]