Computing Similarity Distances Between Rankings

TL;DR

This paper introduces efficient algorithms for computing similarity-aware distances between rankings, considering candidate similarities, with applications across various fields such as bioinformatics and social sciences.

Contribution

It presents novel quadratic-time algorithms for minimum cost cycle decompositions and approximations for permutations with multiple cycles, leveraging tree structures and shortest path methods.

Findings

Quadratic-time algorithm for simple cycle decomposition.

A 4/3-approximation algorithm for complex permutations.

New cycle balancing and merging techniques based on tree metrics.

Abstract

We address the problem of computing distances between rankings that take into account similarities between candidates. The need for evaluating such distances is governed by applications as diverse as rank aggregation, bioinformatics, social sciences and data storage. The problem may be summarized as follows: Given two rankings and a positive cost function on transpositions that depends on the similarity of the candidates involved, find a smallest cost sequence of transpositions that converts one ranking into another. Our focus is on costs that may be described via special metric-tree structures and on complete rankings modeled as permutations. The presented results include a quadratic-time algorithm for finding a minimum cost decomposition for simple cycles, and a quadratic-time, $4/3$-approximation algorithm for permutations that contain multiple cycles. The proposed methods rely on investigating a newly introduced balancing property of cycles embedded in trees, cycle-merging methods, and shortest path optimization techniques.