Counting overlapping pairs of words

TL;DR

This paper develops recurrence relations to count pairs of words sharing the same overlap correlation, providing asymptotic results and bounds that advance understanding of word overlaps with applications in bioinformatics and string algorithms.

Contribution

It introduces a novel method to compute the number of word pairs with specific overlaps, solving open questions about the expected overlap length and asymptotic ratios.

Findings

Derived recurrences for population size of word pairs with given correlation

Proved asymptotic convergence of the expected longest border length

Established bounds for the asymptotic population ratio of correlations

Abstract

A correlation is a binary vector that encodes all possible positions of overlaps of two words, where an overlap for an ordered pair of words (u,v) occurs if a suffix of word u matches a prefix of word v. As multiple pairs can have the same correlation, it is relevant to count how many pairs of words share the same correlation depending on the alphabet size and word length n. We exhibit recurrences to compute the number of such pairs -- which is termed population size -- for any correlation; for this, we exploit a relationship between overlaps of two words and self-overlap of one word. This theorem allows us to compute the number of pairs with a longest overlap of a given length and to show that the expected length of the longest border of two words asymptotically converges, which solves two open questions raised by Gabric in 2022. Finally, we also provide bounds for the asymptotic of the population ratio of any correlation. Given the importance of word overlaps in areas like word combinatorics, bioinformatics, and digital communication, our results may ease analyses of algorithms for string processing, code design, or genome assembly.