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Related papers: The magnitude of metric spaces

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Magnitude is a numerical isometric invariant of metric spaces, whose definition arises from a precise analogy between categories and metric spaces. Despite this exotic provenance, magnitude turns out to encode many invariants from integral…

Metric Geometry · Mathematics 2017-09-05 Tom Leinster , Mark W. Meckes

Magnitude is a numerical invariant of compact metric spaces. Its theory is most mature for spaces satisfying the classical condition of being of negative type, and the magnitude of such a space lies in the interval $[1, \infty]$. Until now,…

Metric Geometry · Mathematics 2023-11-30 Tom Leinster , Mark Meckes

Magnitude is a numerical invariant of metric spaces introduced by Leinster, motivated by considerations from category theory. This paper extends the original definition for finite spaces to compact spaces, in an equivalent but more natural…

Metric Geometry · Mathematics 2015-07-22 Mark W. Meckes

Magnitude is a canonical invariant of finite metric spaces which has its origins in category theory; it is analogous to cardinality of finite sets. Here, by approximating certain compact subsets of Euclidean space with finite subsets, the…

Metric Geometry · Mathematics 2013-02-14 Tom Leinster , Simon Willerton

Magnitude is a numerical invariant of finite metric spaces, recently introduced by T. Leinster, which is analogous in precise senses to the cardinality of finite sets or the Euler characteristic of topological spaces. It has been extended…

Metric Geometry · Mathematics 2013-08-27 Mark W. Meckes

Magnitude is a numerical invariant of enriched categories, including in particular metric spaces as $[0,\infty)$-enriched categories. We show that in many cases magnitude can be categorified to a homology theory for enriched categories,…

Algebraic Topology · Mathematics 2021-11-10 Tom Leinster , Michael Shulman

The magnitude of a metric space is a novel invariant that provides a measure of the 'effective size' of a space across multiple scales, while also capturing numerous geometrical properties, such as curvature, density, or entropy. We develop…

Machine Learning · Computer Science 2025-01-16 Katharina Limbeck , Rayna Andreeva , Rik Sarkar , Bastian Rieck

The magnitude of a graph is one of a family of cardinality-like invariants extending across mathematics; it is a cousin to Euler characteristic and geometric measure. Among its cardinality-like properties are multiplicativity with respect…

Combinatorics · Mathematics 2019-02-20 Tom Leinster

Magnitude, obtained as a special case of Euler characteristic of enriched category, represents a sense of the size of metric spaces and is related to classical notions such as cardinality, dimension, and volume. While the studies have…

Machine Learning · Statistics 2025-09-16 Byungchang So

Magnitude is an isometric invariant for metric spaces that was introduced by Leinster around 2010, and is currently the object of intense research, since it has been shown to encode many known invariants of metric spaces. In recent work,…

Algebraic Topology · Mathematics 2026-03-03 Miguel O'Malley , Sara Kalisnik , Nina Otter

We propose a definition of magnitude for a length space with a Borel measure, which involves integrals over the set of geodesics. This quantity agrees with the magnitude of finite metric spaces, up to re-scaling the metric to ensure the…

Differential Geometry · Mathematics 2026-05-25 Yoshinori Hashimoto

Magnitude is a numerical invariant of metric spaces and graphs, analogous, in a precise sense, to Euler characteristic. Magnitude homology is an algebraic invariant constructed to categorify magnitude. Among the important features of the…

Combinatorics · Mathematics 2024-04-11 Emily Roff

The Euler characteristic is an invariant of a topological space that in a precise sense captures its canonical notion of size, akin to the cardinality of a set. The Euler characteristic is closely related to the homology of a space, as it…

Algebraic Topology · Mathematics 2022-06-22 Nina Otter

Deep learning models have seen significant successes in numerous applications, but their inner workings remain elusive. The purpose of this work is to quantify the learning process of deep neural networks through the lens of a novel…

Machine Learning · Computer Science 2023-05-10 Rayna Andreeva , Katharina Limbeck , Bastian Rieck , Rik Sarkar

Magnitude is a measure of size defined for certain classes of metric spaces; it arose from ideas in category theory. In particular, magnitude is defined for compact subsets of Euclidean space and, in arXiv:1507.02502, Barcel\'o and Carbery…

Metric Geometry · Mathematics 2018-04-09 Simon Willerton

There is a general notion of the magnitude of an enriched category, defined subject to hypotheses. In topological and geometric contexts, magnitude is already known to be closely related to classical invariants such as Euler characteristic…

Rings and Algebras · Mathematics 2016-01-11 Joseph Chuang , Alastair King , Tom Leinster

Magnitude of a finite metric space and the related notion of magnitude functions on metric spaces is an active area of research in algebraic topology. Magnitude originally arose in the context of biology, where it represents the number of…

Algebraic Topology · Mathematics 2019-08-08 Glenn Fung , Eric Bunch , Dan Dickinson

Magnitude is a numerical invariant of compact metric spaces, originally inspired by category theory and now known to be related to myriad other geometric quantities. Generalizing earlier results in $\ell_1^n$ and Euclidean space, we prove…

Metric Geometry · Mathematics 2023-11-30 Mark W. Meckes

The main result of this article is a geometric interpretation of magnitude, a real-valued invariant of metric spaces. We introduce a Euclidean embedding of a (suitable) finite metric space $X$ such that the magnitude of $X$ can be expressed…

Metric Geometry · Mathematics 2025-10-22 Karel Devriendt

Magnitude is a real-valued invariant of metric spaces which, in the finite setting, can be understood as recording the 'effective number of points' in a space as the scale of the metric varies. Motivated by applications in topological data…

Metric Geometry · Mathematics 2025-01-28 Hirokazu Katsumasa , Emily Roff , Masahiko Yoshinaga
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