YodaOS VS Cytoscape.js

YodaOS - AI operating system.

This could be probably done with https://js.cytoscape.org/ as well, so maybe look into that.

Maybe look into Cytoscape.js... There's a react wrapper component: https://github.com/plotly/react-cytoscapejs

The UI is almost entirely based on Cytoscape JS, which is one of the most use graph libraries out there (and for good reason, I've found it very good). The UI is similar to some other JavaScript libraries that deal with visualisation of network models, so it ends up being similar to most examples of Neo4j dashboards, D3, etc.

Relationship graphs are created using Cytoscape.js

I am playing with an app built around cytoscape.js for visualizing graphs. That means adding/removing nodes, responding to node events and so on.

Data source: DB of tutorials and tags. I created the skills map of collective knowledge shows a number of tutorials uploaded by users. It is illustrated the topics of user-generated content. Size of stars is a number of tutorials on the topic. The bigger the star — the more popular skill. The more connection a skill has — the more essential and versatile it is. After the login, this map becomes personalized for every user in accordance with the uploaded tutorials. All skills are combined into 6 specializations (science, sign, people, tech, art, business). Some topics are repeated in several specializations, for instance, "soft skills." Tool: Cytograph.js Layout: Cise layout Interactive version is here https://unschooler.me/skills

> Yeah, I know there's D3.js but that involved way more knowledge and learning than I was interested in.

Same, d3 looked very powerful but had a steep learning curve. I was looking for something simple to generate process trees in real time and ended up using cytoscape js [0], helped me have a working POC in an hour, highly recommended.

[0] https://js.cytoscape.org/

Well, I'll write the post on how it was created. In short, the visualization itself is mostly a https://js.cytoscape.org/ with a few custom CSS options. The hardest thing was to figure out how to prepare the data.We had at the row data: