[tantalor]

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

This makes the graph look more random/noisy/disorganized than it actually is.

[peppery]

Since 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/ ) 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.

[theophilec]

I 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]

FYI I updated the visualization. Credit goes to @jacomyal from @ouestware on GitHub who helped me out.

[tantalor]

The "Chloé Woestelandt" test is still just as bad

[theophilec]

I see this: https://imgur.com/a/SGnbXjG

[tantalor]

Yes! 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]

Yeah, they should have used UMAP or tSNE to cluster the data a bit