grape
deepscatter
| grape | deepscatter | |
|---|---|---|
| 3 | 4 | |
| 482 | 975 | |
| 2.9% | 1.9% | |
| 6.4 | 7.7 | |
| 2 months ago | 19 days ago | |
| Jupyter Notebook | TypeScript | |
| MIT License | GNU General Public License v3.0 or later |
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grape
- Grape (Graph Representation LeArning, Predictions and Evaluation)
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Zoomable, animated scatterplots in the browser that scales over a billion points
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/
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[P] We are building a curated list of open source tooling for data-centric AI workflows, looking for contributions.
For graph embeddings, there's quite a few. I'd recommend this one, but there's also this one (disclaimer: I'm the author) or this one, more of a DGL library.
deepscatter
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A Visual Book Recommender
similiar t-SNE visualisation just for papers:
https://static.nomic.ai/pubmed.html
using Nomic Ai deepscatter
https://github.com/nomic-ai/deepscatter
- Zoomable, animated scatterplots in the browser that scales over a billion points
- DeepScatter – render billions of points in the browser
What are some alternatives?
deodel - A mixed attributes predictive algorithm implemented in Python.
pix-image-viewer - Desktop image viewer. View thousands of images in a zoomable, pannable grid.
refinery - The data scientist's open-source choice to scale, assess and maintain natural language data. Treat training data like a software artifact.
nodevectors - Fastest network node embeddings in the west
dgl - Python package built to ease deep learning on graph, on top of existing DL frameworks.
nanocube
galapix
cleanlab - The standard data-centric AI package for data quality and machine learning with messy, real-world data and labels.
gpt4all - gpt4all: run open-source LLMs anywhere
awesome-production-machine-learning - A curated list of awesome open source libraries to deploy, monitor, version and scale your machine learning
RasterFairy - The purpose of Raster Fairy is to transform any kind of 2D point cloud into a regular raster whilst trying to preserve the neighborhood relations that were present in the original cloud. A typical use case is if you have a similarity clustering of images and want to show the images in a regular table structure.