GEM VS LAGraph

Compare GEM vs LAGraph and see what are their differences.

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)
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GEM LAGraph
1 3
1,261 221
- 2.3%
0.0 8.2
5 months ago 6 days ago
Python C
BSD 3-clause "New" or "Revised" License GNU General Public License v3.0 or later
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.

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
    4 projects | /r/MachineLearning | 4 Jan 2021
    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.

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.

What are some alternatives?

When comparing GEM and LAGraph you can also consider the following projects:

node2vec-c - node2vec implementation in C++

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.

karateclub - Karate Club: An API Oriented Open-source Python Framework for Unsupervised Learning on Graphs (CIKM 2020)