collaborators

6 papers

cs.CG2026

Discrete homology computations by reduction to zero differentials

Sterling Ebel, Chris Kapulkin, Nathan Kershaw

We develop a new algorithm for computing (persistent) discrete homology of graphs using reduction to zero differentials and active enumeration. This allows us to compute the fourth…

math.AT2026

Topological data analysis using persistent discrete homology

Chris Kapulkin, Nathan Kershaw

We propose persistent discrete homology as a tool for topological data analysis and discuss its advantages over the existing methods. In particular, we provide empirical evidence t…

cs.CG2026

RedZeD: Computing persistent homology by Reduction to Zero Differentials

Chris Kapulkin, Nathan Kershaw

We introduce a new algorithm for computing persistent homology of Vietoris--Rips filtrations, which in many cases offers a considerable improvement both in terms of time and memory…

math.CT2025

Closed symmetric monoidal structures on the category of graphs

Chris Kapulkin, Nathan Kershaw

We show that the category of (reflexive) graphs and graph maps carries exactly two closed symmetric monoidal products: the box product and the categorical product.

math.DS2025

Categorical foundations of discrete dynamical systems

Daniel Carranza, Chris Kapulkin, Nathan Kershaw +2

We develop categorical foundations of discrete dynamical systems, aimed at understanding how the structure of the system affects its dynamics. The key technical innovation is the n…

cs.CG2025

Faster computations of discrete homology

Chris Kapulkin, Nathan Kershaw

Machine computation of the discrete homology of graphs has stopped at degree two. We present an algorithm that reaches degree four. It generates the singular cubes inductively, pai…