BotLibre
grape
| BotLibre | grape | |
|---|---|---|
| 1 | 3 | |
| 561 | 483 | |
| -0.7% | 3.1% | |
| 6.6 | 6.4 | |
| about 1 month ago | 3 months ago | |
| Java | Jupyter Notebook | |
| Eclipse Public License 1.0 | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
BotLibre
grape
- Grape (Graph Representation LeArning, Predictions and Evaluation)
-
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/
-
[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.
What are some alternatives?
learn - Neuro-symbolic interpretation learning (mostly just language-learning, for now)
deodel - A mixed attributes predictive algorithm implemented in Python.
refinery - The data scientist's open-source choice to scale, assess and maintain natural language data. Treat training data like a software artifact.
DKPro Core - Collection of software components for natural language processing (NLP) based on the Apache UIMA framework.
deepscatter - Zoomable, animated scatterplots in the browser that scales over a billion points
dgl - Python package built to ease deep learning on graph, on top of existing DL frameworks.
simplenlg - Java API for Natural Language Generation. Originally developed by Ehud Reiter at the University of Aberdeen’s Department of Computing Science and co-founder of Arria NLG. This git repo is the official SimpleNLG version.
nanocube
sematle - NLU service that converts plain English to known and structured data.
cleanlab - The standard data-centric AI package for data quality and machine learning with messy, real-world data and labels.