Paper
Graph embeddings
Blogpost
How We Handle Billion-Scale Graph Data

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Cleora PRO helps Data Science and Analytics teams create top quality embeddings without access to expensive hardware.
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About Cleora

A machine learning tool that enables faster and hyper-easy production of graph embeddings for big graphs

Cleora embeds entities in n-dimensional spherical spaces utilizing extremely fast, stable, and iterative random projections, which allows for unparalleled performance and scalability. Types of data which can be embedded include for example:
purchase events from e-commerce companies, banks, telco companies, and other businesses
click, page view, and other page navigation event data
card transaction and bank transfer events
textual data

200x faster than DeepWalk,
4x-8x faster than Pytorch-BigGraph by Facebook

Cleora computes embeddings of your relational data. Entities such as clients, products, stores, accounts, and others can be represented with embeddings, just like Word2Vec or BERT for text or CLIP for images. Cleora embeddings are behavioral - they represent entities by their behavior history, which has the form of large graphs.

What can you build with Cleora Embeddings?

  • Recommender Systems
  • Client Segmentation
  • Propensity Prediction
  • Lifetime Value Modeling
  • Churn Prediction
  • and many other types of enterprise models
Cleora PRO (Enterprise) vs Cleora Open Source

Self-service Cleora PRO is now available for selected customers

Cleora Open Source is publicly available on Github and used by many industry leaders.

Key improvements in Cleora PRO over the open source version:
automatic scaling: no expensive hardware required
ease of use: only 3 columns extracted from your DB are required. Graphs are detected automatically in the data
performance optimizations: 10x faster embedding times
latest research: significantly improved embedding quality
new feature: item attributes are supported
Join the private beta for Cleora.ai PRO

Embedding quality

The task is to predict the existence of edges in the graph. For example, predicting whether a certain product will be bought by a certain customer. Higher score is better.

Embedding speed

Total time of computing the embeddings.

Key technical features of Cleora embeddings

The embeddings produced by Cleora are different from those produced by Node2vec, Word2vec, DeepWalk or other systems in this class by a number of key properties:

Efficiency

Cleora is two orders of magnitude faster than Node2Vec or DeepWalk. We’ve embedded graphs with 100s of billions of edges on a single machine without GPUs. It likely is the fastest approach possible.

Inductivity

As Cleora embeddings of an entity are defined only by interactions with other entities, vectors for new entities can be computed on-the-fly.

Cross-dataset compositionality

Thanks to stability of Cleora embeddings, embeddings of the same entity on multiple datasets can be combined by averaging, yielding meaningful vectors.

Stability

All starting vectors for entities are deterministic, which means that Cleora embeddings on similar datasets will end up being similar. Methods like Word2vec, Node2vec or DeepWalk return different results with every run.

Extreme parallelism and performance

Cleora is written in Rust utilizing thread-level parallelism for all calculations except input file loading. In practice this means that the embedding process is often faster than loading the input data.

Dim-wise independence

Thanks to the process producing Cleora embeddings, every dimension is independent of others. This property allows for efficient and low-parameter method for combining multi-view embeddings with Conv1d layers.

We used Cleora for customer-restaurants graph data in the National Capital Region (NCR) area. And to our delight, the embedding generation was superfast (i.e <5 minutes). For context, do remember that GraphSAGE took ~20hours for the same data in the NCR region.

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