Everything Cleora Can Do
A comprehensive overview of every capability packed into a single 5 MB package — no GPU, no heavy dependencies.
Embedding Engine
8 algorithms unified under one API — spectral, walk-based, and matrix factorization methods
Rust Performance Core
Sparse matrix operations in Rust with PyO3 bindings. Adaptive parallelism across all CPU cores. 240x faster than GraphSAGE on large graphs.
Heterogeneous & Hypergraphs
Multi-type nodes and edges with per-relation embeddings. HeteroGraph class, metapath-based embedding, and homogeneous export for real-world data that doesn't fit simple graphs.
Classification
Node classification without PyTorch or TensorFlow. Pure numpy/scipy implementations that run anywhere.
Community Detection
Discover clusters and communities in your graph using multiple algorithms with modularity scoring.
Graph Statistics
Compute structural properties and centrality measures directly from your graph.
Preprocessing
Clean and prepare your graph data before embedding.
Similarity Search
Find similar entities using brute-force or approximate nearest neighbors. Predict missing links in your graph.
Embedding Compression
Reduce embedding dimensionality and memory footprint without losing signal.
Alignment & Ensemble
Align embedding spaces and combine multiple embeddings for stronger representations.
Evaluation & Metrics
Comprehensive evaluation without leaving the library. Measure embedding quality across multiple tasks.
Graph Sampling
Sample subgraphs for scalable training and evaluation on large graphs.
I/O & Interop
Import from and export to popular data science formats. Seamless integration with your existing stack.
Visualization
Reduce dimensions and plot embeddings for exploration and debugging.
Datasets
14+ built-in datasets for benchmarking and experimentation — ready to use with a single call.
Synthetic Generators
Generate synthetic graphs with known properties for testing and experimentation.
Hyperparameter Tuning
Find the optimal embedding configuration with automatic evaluation across all algorithms.
Benchmarking
Compare algorithms across datasets with time, memory, and accuracy metrics. Publication-ready results.
CLI
Embed graphs directly from the command line — perfect for scripting and CI/CD pipelines.
Scikit-learn Compatible
CleoraEmbedder implements the scikit-learn estimator API — fit(), transform(), fit_transform(). Works with sklearn pipelines and grid search.
Deterministic & Reproducible
Identical results on every run. Seeded initialization guarantees reproducibility across platforms and runs — critical for research and production.
Tiny Footprint
Just 5 MB installed — only numpy and scipy required. Compare to 500 MB+ for PyTorch Geometric or DGL. Installs in seconds, not minutes.
Supervised Fine-tuning
Refine embeddings with labeled positive/negative pairs using margin loss. Adapt pre-trained embeddings to your specific task without retraining from scratch.