What is Cleora.ai?
Cleora is a Rust-powered graph embedding engine that redefines how graph embeddings are computed. Unlike traditional libraries that rely on random walk sampling, negative sampling, and GPU clusters, Cleora computes the exact distribution of all possible walks in a single sparse matrix power operation. This deterministic approach eliminates stochastic noise, ensuring perfect reproducibility and blazing-fast performance on a single CPU core.
Key features include native support for heterogeneous hypergraphs with typed, multi-relational edges. The input is a simple TSV file with typed columns, requiring no graph preprocessing. The algorithm constructs a sparse transition matrix from the graph and iteratively multiplies the embedding matrix by this transition matrix. Each iteration captures the full distribution of walks of length k, replacing each node's embedding with the L2-normalized average of its neighbors' embeddings. For co-occurrence similarity, 3-4 iterations suffice; for contextual similarity like skip-gram, 7+ iterations are recommended.
Cleora's benefits are substantial: it is parameter-free, requiring no hyperparameter tuning, and its Rust core with adaptive parallelism ensures every CPU cycle is utilized. The library is lightweight, approximately 5 MB, with dependencies only on numpy and scipy—contrasting sharply with PyTorch Geometric's 500+ MB footprint. Use cases include recommendation systems, link prediction, node classification, and any application requiring high-quality, deterministic embeddings from large-scale graphs. For example, Zomato generated embeddings in under 5 minutes with Cleora, compared to 20 hours with GraphSAGE on the same dataset.
Technical details: Cleora's deterministic sparse Markov propagation with interleaved whitening ensures that the same input always yields the same output, critical for reproducible research and production ML pipelines. The engine handles multi-type nodes and edges, bipartite graphs, and hypergraphs natively. With no random walk sampling or skip-gram training, Cleora achieves orders-of-magnitude speed improvements and highest accuracy on real-world graphs where other methods score single digits.
Use Cases
Key Features
graph embedding engine Rust powered deterministic computation heterogeneous hypergraph support typed multi relational edges no hyperparameter tuning single CPU core exact walk distribution sparse matrix power operation perfect reproducibilityWho is it for?
data scientists, machine learning engineers, graph analytics researchers, NLP practitioners, recommendation system developers, bioinformaticians
Similar Tools
Details
UX-Ray 1.0 by Baymard is a powerful AI-driven tool that provides instant UX analysis for any ecommer...
freemium
Details
You.com offers a suite of AI-ready Web Search APIs designed to power next-generation AI agents, LLMs...
Paid
Details
Law Insider is an AI-powered contract drafting, review, and redlining platform designed for legal pr...
Subscription
Details
StudyCrumb’s essay rephraser is a high-end, free online tool designed to help students paraphrase ...
Free