What is Hyperbolic Embeddings

Hyperbolic Embeddings: A New Approach to Machine Learning

In the world of machine learning, there are many algorithms that can be used to analyze and understand data. One popular technique is called embedding, which involves mapping data into a lower-dimensional space where it can be more easily manipulated and analyzed. While embedding has proven to be a useful technique in many applications, traditional embedding methods are limited by their reliance on Euclidean space. However, recent research has shown that using hyperbolic space for embedding can provide significant advantages over Euclidean embedding, particularly for data that exhibits hierarchical structure.

In this article, we will explore the concept of hyperbolic embeddings, their benefits and drawbacks, and some potential applications of this exciting new approach to machine learning.

What is Hyperbolic Space?

To understand hyperbolic embeddings, we first need to understand the concept of hyperbolic space. Hyperbolic space is a type of non-Euclidean geometry, meaning it has different rules and properties than the Euclidean geometry we are all familiar with. In hyperbolic space, the sum of the angles in a triangle is always less than 180 degrees, which contrasts with Euclidean space, where the sum of angles is always 180 degrees. Hyperbolic space can also be characterized by its curvature, which is negative. This means that in hyperbolic space two parallel lines will eventually diverge from each other, unlike in Euclidean space where parallel lines never meet.

While this may seem like an abstract concept, hyperbolic space has been used to describe many real-world phenomena, from the structure of the brain to the social networks of online communities. The benefits of using hyperbolic space for embedding are based on these unique geometrical properties.

Hyperbolic Embeddings

Hyperbolic embeddings involve mapping data points onto a hyperbolic space, rather than a Euclidean space. This approach allows for more natural representations of hierarchical data structures that are often found in real-world data sets. To understand how hyperbolic embeddings work, consider the example of the World Wide Web. Web pages can be represented as nodes in a graph, where the edges between nodes represent links between web pages. In Euclidean space, this graph would be represented as a flat plane, which can obscure the hierarchical structure of the web.

In hyperbolic space, however, the graph can be represented naturally in a curved plane, where nodes that are close together are more closely related, and nodes that are far apart are less related. This allows for more accurate clustering of related web pages, and more accurate recommendations for users based on their browsing history. Another example of the benefits of hyperbolic embeddings can be seen in the analysis of text data. In traditional Euclidean embeddings, words are mapped onto a high-dimensional space, with each dimension representing a different feature of the word. However, hyperbolic embeddings can represent the hierarchical relationships between words much more effectively, resulting in more accurate analyses of text data.

Applications of Hyperbolic Embeddings

Hyperbolic embeddings have numerous applications in machine learning, particularly in areas where hierarchical relationships are important. Some potential uses for hyperbolic embeddings include:

  • Social Network Analysis: Hyperbolic embeddings can be used to analyze the structure of online social networks, and to identify communities and influencers within those networks.
  • Recommender Systems: Hyperbolic embeddings can be used to more accurately recommend products or services to users based on their browsing history and preferences.
  • Natural Language Processing: Hyperbolic embeddings can be used to analyze the structure of text data, resulting in more accurate sentiment analysis and text classification.
  • Brain Mapping: Hyperbolic embeddings can be used to map and understand the structure of the brain, resulting in more advanced diagnoses and treatments for neurological conditions.
Drawbacks of Hyperbolic Embeddings

While hyperbolic embeddings have many potential benefits, they are not without their drawbacks. One major issue with hyperbolic embeddings is the increased complexity compared to Euclidean embeddings. Hyperbolic space is less intuitive and less familiar than Euclidean space, which can make it more difficult for developers and researchers to work with.

Additionally, hyperbolic embeddings require specialized algorithms and techniques, which can make them less accessible to those without experience in the field. However, as the benefits of hyperbolic embeddings become more widely recognized, it is likely that more tools and resources will become available to make them more accessible to a wider audience.


Hyperbolic embeddings represent a new and exciting approach to machine learning, with many potential applications in a variety of fields. While they are not without their drawbacks, hyperbolic embeddings have already shown promising results in analyzing and understanding complex data structures.

As the field of machine learning continues to evolve, it is likely that hyperbolic embeddings will become an increasingly important tool for developers and researchers seeking to extract insights from complex data sets.