GEM
cleora
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GEM | cleora | |
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1 | 8 | |
1,265 | 472 | |
- | 0.6% | |
0.0 | 2.4 | |
6 months ago | 6 months ago | |
Python | Jupyter Notebook | |
BSD 3-clause "New" or "Revised" License | GNU General Public License v3.0 or later |
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
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[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.
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?
sursis - A [personal]<-[notebook]->[network]. Complete with custom numerics for constrained Gaussian gravitation physics.
i3status-rust - Very resourcefriendly and feature-rich replacement for i3status, written in pure Rust
karateclub - Karate Club: An API Oriented Open-source Python Framework for Unsupervised Learning on Graphs (CIKM 2020)
Owlyshield - Owlyshield is an EDR framework designed to safeguard vulnerable applications from potential exploitation (C&C, exfiltration and impact).
node2vec-c - node2vec implementation in C++
textsynth - A (unofficial) Rust wrapper for the TextSynth API.
finalfusion-rust - finalfusion embeddings in Rust
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