GEM
By palash1992
node2vec-c
node2vec implementation in C++ (by xgfs)
Our great sponsors
| GEM | node2vec-c | |
|---|---|---|
| 1 | 1 | |
| 1,265 | 50 | |
| - | - | |
| 0.0 | 0.0 | |
| 6 months ago | almost 4 years ago | |
| Python | C++ | |
| BSD 3-clause "New" or "Revised" License | 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.
GEM
Posts with mentions or reviews of GEM.
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
Besides, they implemented a fast C++ version of the code that works for much larger graphs. If one searches for ProNE's implementation, they would (hypothetically) find the scikit-style wrapper instead of the fully-functional release. It reminds me of a situation with HOPE, when authors of one survey "implemented" it as naive SVD (https://github.com/palash1992/GEM/blob/master/gem/embedding/hope.py#L68) instead of Jacobi-Davidson generalized solver described in the paper (and literally with code released!!). In the end, I would assume that poor paper was less cited because of that repackaging effort.
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 GEM 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.
sursis - A [personal]<-[notebook]->[network]. Complete with custom numerics for constrained Gaussian gravitation physics.
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
karateclub - Karate Club: An API Oriented Open-source Python Framework for Unsupervised Learning on Graphs (CIKM 2020)