pygraphistry

We regularly use pygraphistry to generate /import => viz 100k+ entity embeddings on mobile fine: https://github.com/graphistry/pygraphistry , should be pretty easy to redo / scale . WebGL is impressively capable on even old smartphones nowadays ! More fun, in umap mode, by default, it also shows the top-n similarity edges between each entity, so you get interactive graphs that you can interactively filter + recluster + restyle on diff data attributes, vs just the typical x/y scatter plot (Am on mobile, so haven't been able to load the original...)

We make it pretty easy to go from networkx or any other pydata (DF, csv, parquet, ...) to interactive GPU viz w all sorts of analytics built in: https://github.com/graphistry/pygraphistry#explore-any-data-...

Not sure on easygraph, but we have gpu graph viz generation 1-liners for pandas/nx/igraph/cugraph/parquet/arrow @ http://github.com/graphistry/pygraphistry . If easygraph works with the rest of the pydata world, I'd hope just another 1-liner away. We're now working on louie.ai to make using them all together even easier, all the way to systems like splunk & databricks :)

I appreciate the good faith attempt! https://github.com/graphistry/pygraphistry No 3d/vr/etc unfortunately, we never saw commercial demand in our segments, just looking at bigger datasets and increasingly 'wide' / high-dimensional/scattered/heterogeneous data using accelerated viz & AI, and now text2code And yes, we currently get used by data scientists and devs on problems like supply chain analysis, misinformation, cybersecurity, human trafficking. Seeing 100x+ more data than d3 and having a full env there makes analyst investigations easier. Our original GPU client <> GPU cloud tech helped lead to what is now Apache Arrow (we contributed the JS tier as part of the GPU Open Analytics Initiative) and Nvidia RAPIDS (we wrote the precursor in nodejs/opencl, and worked with Nvidia to restart for pydata), and are now focusing on the Nvidia M

I'd be curious if someone loaded these into http://github.com/graphistry/pygraphistry and tried something like overlaying a GNN anomaly detection over the base visuals. Both parts are automated & GPU accelerated, so can probably handle some decently sized projects!