Zoomable, animated scatterplots in the browser that scales over a billion points

• deepscatter

  4 965 7.8 TypeScript

  Zoomable, animated scatterplots in the browser that scales over a billion points

  

• gpt4all

  139 63,641 9.8 C++

  gpt4all: run open-source LLMs anywhere

  

Nomic are also the ones behind gpt4all. Such a powerhouse team. https://github.com/nomic-ai/gpt4all

• WorkOS

  workos.com sponsored

  The modern identity platform for B2B SaaS. The APIs are flexible and easy-to-use, supporting authentication, user identity, and complex enterprise features like SSO and SCIM provisioning.

  

• nodevectors

  8 487 0.0 Python

  Fastest network node embeddings in the west

Ideally, you'd embed the graph into 2 or 3d first, then visualize it as a scatterplot.

Visualizing the edges at scale doesnt yield nice results in general.

The way to do it is to reduce the graph to some 300d or 500d embeddings, then use TSNE/UMAP/PACMAP to reduce that to 3d. Then visualize.

My prefered way is to use some first order embedding method like GGVec in this library [1] (disclaimer I wrote it). Node2Vec and ProNE don't yield great embeddings for visualization (the first is too filamented, the second too close to the unit ball).

Another great library to do this work is GRAPE [2]. Try first-order embedding methods, or short walks on second order methods to avoid the embeddings being too filamented by long random walk sampling.

[1] https://github.com/VHRanger/nodevectors

[2] https://github.com/AnacletoLAB/grape/

• grape

  3 477 6.4 Jupyter Notebook

  🍇 GRAPE is a Rust/Python Graph Representation Learning library for Predictions and Evaluations (by AnacletoLAB)

  

Ideally, you'd embed the graph into 2 or 3d first, then visualize it as a scatterplot.

Visualizing the edges at scale doesnt yield nice results in general.

The way to do it is to reduce the graph to some 300d or 500d embeddings, then use TSNE/UMAP/PACMAP to reduce that to 3d. Then visualize.

My prefered way is to use some first order embedding method like GGVec in this library [1] (disclaimer I wrote it). Node2Vec and ProNE don't yield great embeddings for visualization (the first is too filamented, the second too close to the unit ball).

Another great library to do this work is GRAPE [2]. Try first-order embedding methods, or short walks on second order methods to avoid the embeddings being too filamented by long random walk sampling.

[1] https://github.com/VHRanger/nodevectors

[2] https://github.com/AnacletoLAB/grape/

• nanocube

  1 729 1.5 C

nice, https://github.com/laurolins/nanocube is/was another interesting approach to the many points issue, though it's seen no updates since 21 afaik

• galapix

  1 - -

• pix-image-viewer

  2 139 2.7 Rust

  Desktop image viewer. View thousands of images in a zoomable, pannable grid.

  

• InfluxDB

  www.influxdata.com sponsored

  Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.

  

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