Tree Path Majority Data Structures

TL;DR

This paper introduces the first data structures for efficiently finding $ au$-majorities on tree paths, with linear and succinct space solutions that support fast queries and are practical for large trees.

Contribution

It presents novel data structures for $ au$-majority queries on tree paths, achieving optimal space and query time trade-offs, including succinct-space solutions based on entropy.

Findings

Linear-space data structure with $O((1/\tau)\log^* n \log\log_w \sigma)$ query time.

Space-efficient solutions using $2nH + 4n + o(n)$ bits and $nH + O(n)$ bits, with the same query time.

Construction time for all structures is $O(n\log n)$.

Abstract

We present the first solution to $\tau$-majorities on tree paths. Given a tree of $n$ nodes, each with a label from $[1..\sigma]$, and a fixed threshold $0<\tau<1$, such a query gives two nodes $u$ and $v$ and asks for all the labels that appear more than $\tau \cdot |P_{uv}|$ times in the path $P_{uv}$ from $u$ to $v$, where $|P_{uv}|$ denotes the number of nodes in $P_{uv}$. Note that the answer to any query is of size up to $1/\tau$. On a $w$-bit RAM, we obtain a linear-space data structure with $O((1/\tau)\log^* n \log\log_w \sigma)$ query time. For any $\kappa > 1$, we can also build a structure that uses $O(n\log^{[\kappa]} n)$ space, where $\log^{[\kappa]} n$ denotes the function that applies logarithm $\kappa$ times to $n$, and answers queries in time $O((1/\tau)\log\log_w \sigma)$. The construction time of both structures is $O(n\log n)$. We also describe two succinct-space solutions with the same query time of the linear-space structure. One uses $2nH + 4n + o(n)(H+1)$ bits, where $H \le \lg\sigma$ is the entropy of the label distribution, and can be built in $O(n\log n)$ time. The other uses $nH + O(n) + o(nH)$ bits and is built in $O(n\log n)$ time w.h.p.