Cleora is a fast, deterministic graph embedding engine that computes all random walks in a single matrix multiplication, requiring no GPUs or negative sampling.
Cleora is an open-source graph embedding engine designed for efficient, scalable learning of entity embeddings from heterogeneous relational data. It solves the problem of generating high-quality graph representations quickly and deterministically without requiring GPUs or negative sampling, making it suitable for large-scale production systems.
Data scientists and machine learning engineers working on recommendation systems, knowledge graphs, fraud detection, social network analysis, or any application requiring fast and accurate graph embeddings at scale.
Developers choose Cleora for its unmatched speed and memory efficiency, deterministic outputs ensuring reproducibility, and ability to handle heterogeneous graphs natively—all while maintaining top-tier accuracy compared to competing algorithms.
Cleora AI is a general-purpose open-source model for efficient, scalable learning of stable and inductive entity embeddings for heterogeneous relational data. Created by Synerise.com team.
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Same input always produces identical output, eliminating stochastic variation for reproducible research and production ML pipelines, as emphasized in the README.
Computes all possible random walks exactly via matrix multiplication, resulting in higher accuracy and less noise compared to approximation-based methods like DeepWalk or Node2Vec.
Rust-powered core delivers 240x faster performance than GraphSAGE and uses 50x less memory than NetMF, with a ~5 MB install size and zero GPU requirements, making it ideal for scalable production.
Natively handles multi-type nodes, edges, bipartite graphs, and hypergraphs without preprocessing, simplifying complex graph setups as shown in the TSV input format.
Designed for CPU-only operation, which may not leverage existing GPU infrastructure and could be slower for some intensive tasks compared to GPU-optimized libraries like PyTorch Geometric.
While it includes an MLP classifier, it lacks support for advanced graph neural networks (GNNs) or seamless integration with deep learning frameworks, limiting use in state-of-the-art research.
The absence of stochasticity might reduce robustness in applications where varied embeddings are beneficial, such as in some adversarial training or data augmentation scenarios.