chatgpt-ifplatform
LAGraph
chatgpt-ifplatform | LAGraph | |
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1 | 3 | |
1 | 223 | |
- | 0.9% | |
3.2 | 8.0 | |
about 1 year ago | 5 days ago | |
C# | C | |
Apache License 2.0 | GNU General Public License v3.0 or later |
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chatgpt-ifplatform
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The Hunt for the Missing Data Type
It's really an experimental endeavor. I have a Github repo (https://github.com/ChicagoDave/chatgpt-ifplatform), but I'm still changing things all the time. It's very volatile.
Finding the balance between OO principals, Fluid coding capabilities, separating the data, grammar, parser, and world model and then constructing a standard IF library of common IF "things" is like juggling 20 kittens and 10 chainsaws.
Some things are confounding like do I define a container with a boolean property on an object or is a container a subclass of the base Thing? How does that extend to the underlying graph data store? What will queries look like and which solution is more meaningful to authors?
Seriously, 95% of the fun is figuring all of these things out.
LAGraph
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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).
What are some alternatives?
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.