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jlcx

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jlcx
·tahun lalu·discuss
Thanks for the compliment! The larger graph is basically built by extracting all relationships of certain types (e.g. parent/child, teacher/student, cause/effect) from Wikidata, along with the earliest known date for the items (e.g. date of birth, time of invention/discovery). The layout started as a 3D force-directed layout, but I turned one axis into the timeline. For the visualization, I used (forked) https://github.com/anvaka/pm (which did get some deserved attention here: https://news.ycombinator.com/item?id=40817852 ) and the related ngraph.offline.layout repo.

Trying to answer your final question: there are a lot of things that I should probably improve here, but I've also wondered if this kind of giant graph visualization just doesn't really work for most people.
jlcx
·tahun lalu·discuss
For something related that takes a very different approach: https://causegraph.github.io/causalaxies

In contrast to the author's decisions here, I decided to

-go for an "everything tree" even if that will contain many more errors

-use DBpedia/Wikidata, and address issues discovered by editing Wikipedia/Wikidata

-use a 3D visualization tool, due to the size of the graph

I think it reveals an interesting overall structure, and some interesting details for those who zoom in despite the issues with the data.
jlcx
·tahun lalu·discuss
Speaking of Burke, I believe his book The Pinball Effect has notations in the margins directing the reader to other pages that mention the same node in the graph (whether that's exactly how he thought of it or not). It seems like an interesting attempt to express this non-linear structure in the form of a book.
jlcx
·tahun lalu·discuss
The novella "To Be Taught, If Fortunate," the only Becky Chambers book not part of either of those series, is also good.
jlcx
·2 tahun yang lalu·discuss
3c here as well. The doctors were surprised that it had spread to my bones, and it didn’t show up on the CT scans that they were using for surveillance.