LAGraph
cleora
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LAGraph | cleora | |
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3 | 8 | |
221 | 472 | |
1.4% | 0.6% | |
8.2 | 2.4 | |
10 days ago | 6 months ago | |
C | Jupyter Notebook | |
GNU General Public License v3.0 or later | GNU General Public License v3.0 or later |
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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).
cleora
- Cleora - an ultra fast graph embedding tool written in Rust
- Cleora.ai - open source general-purpose model for efficient, scalable learning of stable and inductive entity embeddings for heterogeneous relational data - new updates
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[R] Cleora: A Simple, Strong and Scalable Graph Embedding Scheme
Our team at Synerise AI has open sourced Cleora - an ultra fast vertex embedding tool for graphs & hypergraphs. If you've ever used node2vec, DeepWalk, LINE or similar methods - it might be worth to check it out.
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[R] Cleora - the fastest graph & hypergraph node embedding tool
A few weeks ago, our team at Synerise AI has open sourced Cleora - an ultra fast vertex embedding tool for graphs & hypergraphs. It is a tool, which can ingest any categorical, relational data and turn it into vector embeddings of entities. It is extremely fast, while offering very competitive quality of results. In fact, it may be the fastest hypergraph embedding tool possible in practice, without intentionally discarding/reducing input data.
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[D] Why I'm Lukewarm on Graph Neural Networks
Thanks for raising so many interesting points about model performance and complexity. In this context, I think our newly released graph embedding library - Cleora - might be of interest: https://github.com/Synerise/cleora Cleora has some nice performance-wise properties:
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Rusticles #20 - Wed Nov 18 2020
Synerise/cleora (Rust): Cleora AI is a general-purpose model for efficient, scalable learning of stable and inductive entity embeddings for heterogeneous relational data.
What are some alternatives?
node2vec-c - node2vec implementation in C++
i3status-rust - Very resourcefriendly and feature-rich replacement for i3status, written in pure Rust
Owlyshield - Owlyshield is an EDR framework designed to safeguard vulnerable applications from potential exploitation (C&C, exfiltration and impact).
textsynth - A (unofficial) Rust wrapper for the TextSynth API.
finalfusion-rust - finalfusion embeddings in Rust
GEM
yourcontrols - Shared cockpit for Microsoft Flight Simulator.
dog - A command-line DNS client.
ggez - Rust library to create a Good Game Easily
PyO3 - Rust bindings for the Python interpreter
Rustlings - :crab: Small exercises to get you used to reading and writing Rust code!