Tree Edit Distance with Variables. Measuring the Similarity between Mathematical Formulas

TL;DR

This paper introduces a novel tree edit distance model with variables to measure similarity between mathematical formulas, analyzing its computational complexity and applying it to biological system models.

Contribution

It extends tree edit distance to handle trees with variables, providing complexity analysis and applications to differential equations in biological models.

Findings

NP-complete for ordered trees

Graph isomorphism complete for unordered trees with zero distance decision

Polynomial time solution when maximum outdegree is bounded

Abstract

In this article, we propose tree edit distance with variables, which is an extension of the tree edit distance to handle trees with variables and has a potential application to measuring the similarity between mathematical formulas, especially, those appearing in mathematical models of biological systems. We analyze the computational complexities of several variants of this new model. In particular, we show that the problem is NP-complete for ordered trees. We also show for unordered trees that the problem of deciding whether or not the distance is 0 is graph isomorphism complete but can be solved in polynomial time if the maximum outdegree of input trees is bounded by a constant. This distance model is then extended for measuring the difference/similarity between two systems of differential equations, for which results of preliminary computational experiments using biological models are provided.