LAGraph
This is a library plus a test harness for collecting algorithms that use the GraphBLAS. For test coverage reports, see https://graphblas.org/LAGraph/ . Documentation: https://lagraph.readthedocs.org (by GraphBLAS)
node2vec-c
node2vec implementation in C++ (by xgfs)
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| LAGraph | node2vec-c | |
|---|---|---|
| 3 | 1 | |
| 221 | 50 | |
| 1.4% | - | |
| 8.2 | 0.0 | |
| 10 days ago | almost 4 years ago | |
| C | C++ | |
| GNU General Public License v3.0 or later | MIT License |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
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.
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.
LAGraph
Posts with mentions or reviews of LAGraph.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2024-03-04.
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The Hunt for the Missing Data Type
> you probably want more specialised tools like BLAS/LAPACK
The GraphBLAS and LAGraph are sparse matrix optimized libraries for this exact purpose:
https://github.com/DrTimothyAldenDavis/GraphBLAS
https://github.com/GraphBLAS/LAGraph/
- A windowed graph Fourier transform
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[D] Why I'm Lukewarm on Graph Neural Networks
I work on GraphBLAS, primarily on its LAGraph library and on tutorials. In the last few years, the GraphBLAS community has made a lot of progress on more efficient sparse matrix algorithms and porting graph algorithms to linear algebra – I hope LAGraph can play the role of a more efficient NetworkX in the future. The output of most LAGraph algorithms is a bunch of vectors/matrices so piping these into machine learning algorithms should be possible (and probably more efficient than using other representations).
node2vec-c
Posts with mentions or reviews of node2vec-c.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2021-01-04.
-
[D] Why I'm Lukewarm on Graph Neural Networks
First, I compared the speed (on a 6-core Mac, once, not scientific benchmarking, beware) of your library and a 3 year old standalone implementation I remember I once linked to you when you were posting about your library here (1 year ago? idk) https://github.com/xgfs/node2vec-c . The timings are (wall time) 17min 48s for your library and 4min 34s for the above code. That's (in?)famous Blogcatalog data, since I had that lying around. Note that there is also a node2vec implementation in SNAP and countless more on github. Is there any benchmark showing your version is faster than them?
What are some alternatives?
When comparing LAGraph and node2vec-c you can also consider the following projects:
cleora - Cleora AI is a general-purpose model for efficient, scalable learning of stable and inductive entity embeddings for heterogeneous relational data.
GEM