BRbryanrasmussen·Sep 28, 2023Submission
Grape (Graph Representation LeArning, Predictions and Evaluation)
2 points · Open discussionAnacletoLAB
grape
Jupyter Notebook2023–2023
AnacletoLAB / grape
grape
🍇 GRAPE is a Rust/Python Graph Representation Learning library for Predictions and Evaluations
2Distinct people
2Mentions
0Explicit recommenders
644GitHub stars
What to know before exploring
What it is
🍇 GRAPE is a Rust/Python Graph Representation Learning library for Predictions and Evaluations
GitHub
Where it is used
No documented product use has been verified yet.
What people are saying
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Also in these discussions
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This week on Hacker News
Nov 7–Nov 14 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/14/2026, 3:56:07 PM UTC.
No direct repository links were found in the indexed HN discussions over the past seven days. Older discussions are available below.
Mentions over time
HN mentions by week2 dated mentions
Observed mentions: 2023-04-10 15:53:12 UTC to 2023-09-28 21:23:07 UTC. Weeks start Monday, UTC.
Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.
View mention counts and date ranges
| Period | Start (UTC) | End (exclusive, UTC) | Mentions | Submissions | Comments |
|---|---|---|---|---|---|
| Apr 10 | 2023-04-10 00:00:00 UTC | 2023-04-17 00:00:00 UTC | 1 | 0 | 1 |
| Apr 17 | 2023-04-17 00:00:00 UTC | 2023-04-24 00:00:00 UTC | 0 | 0 | 0 |
| Apr 24 | 2023-04-24 00:00:00 UTC | 2023-05-01 00:00:00 UTC | 0 | 0 | 0 |
| May 1 | 2023-05-01 00:00:00 UTC | 2023-05-08 00:00:00 UTC | 0 | 0 | 0 |
| May 8 | 2023-05-08 00:00:00 UTC | 2023-05-15 00:00:00 UTC | 0 | 0 | 0 |
| May 15 | 2023-05-15 00:00:00 UTC | 2023-05-22 00:00:00 UTC | 0 | 0 | 0 |
| May 22 | 2023-05-22 00:00:00 UTC | 2023-05-29 00:00:00 UTC | 0 | 0 | 0 |
| May 29 | 2023-05-29 00:00:00 UTC | 2023-06-05 00:00:00 UTC | 0 | 0 | 0 |
| Jun 5 | 2023-06-05 00:00:00 UTC | 2023-06-12 00:00:00 UTC | 0 | 0 | 0 |
| Jun 12 | 2023-06-12 00:00:00 UTC | 2023-06-19 00:00:00 UTC | 0 | 0 | 0 |
| Jun 19 | 2023-06-19 00:00:00 UTC | 2023-06-26 00:00:00 UTC | 0 | 0 | 0 |
| Jun 26 | 2023-06-26 00:00:00 UTC | 2023-07-03 00:00:00 UTC | 0 | 0 | 0 |
| Jul 3 | 2023-07-03 00:00:00 UTC | 2023-07-10 00:00:00 UTC | 0 | 0 | 0 |
| Jul 10 | 2023-07-10 00:00:00 UTC | 2023-07-17 00:00:00 UTC | 0 | 0 | 0 |
| Jul 17 | 2023-07-17 00:00:00 UTC | 2023-07-24 00:00:00 UTC | 0 | 0 | 0 |
| Jul 24 | 2023-07-24 00:00:00 UTC | 2023-07-31 00:00:00 UTC | 0 | 0 | 0 |
| Jul 31 | 2023-07-31 00:00:00 UTC | 2023-08-07 00:00:00 UTC | 0 | 0 | 0 |
| Aug 7 | 2023-08-07 00:00:00 UTC | 2023-08-14 00:00:00 UTC | 0 | 0 | 0 |
| Aug 14 | 2023-08-14 00:00:00 UTC | 2023-08-21 00:00:00 UTC | 0 | 0 | 0 |
| Aug 21 | 2023-08-21 00:00:00 UTC | 2023-08-28 00:00:00 UTC | 0 | 0 | 0 |
| Aug 28 | 2023-08-28 00:00:00 UTC | 2023-09-04 00:00:00 UTC | 0 | 0 | 0 |
| Sep 4 | 2023-09-04 00:00:00 UTC | 2023-09-11 00:00:00 UTC | 0 | 0 | 0 |
| Sep 11 | 2023-09-11 00:00:00 UTC | 2023-09-18 00:00:00 UTC | 0 | 0 | 0 |
| Sep 18 | 2023-09-18 00:00:00 UTC | 2023-09-25 00:00:00 UTC | 0 | 0 | 0 |
| Sep 25 | 2023-09-25 00:00:00 UTC | 2023-10-02 00:00:00 UTC | 1 | 1 | 0 |
What Hacker News says
2 sourcesIdeally, 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. It's simply too noisy.
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/gr