# A Graph Diffusion Algorithm for Lexical Similarity Evaluation

arXiv: https://arxiv.org/abs/2504.06816 · HTML: https://mlsanity.com/paper/2504.06816/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 36 references that are in the map.
- First posted on arXiv 9 Apr 2025.

## Builds on (most cited first, 3 of 3)

- [Novel semi-metrics for multivariate change point analysis and anomaly detection](https://mlsanity.com/paper/1911.00995/) (2019)
- [Generating Feature Vectors from Phonetic Transcriptions in Cross-Linguistic Data Formats](https://mlsanity.com/paper/2405.04271/) (2024)
- [Natural Numerical Networks for Natura 2000 habitats classification by satellite images](https://mlsanity.com/paper/2108.04327/) (2021)

## Abstract (arXiv)

In this paper, we present an algorithm for evaluating lexical similarity between a given language and several reference language clusters. As an input, we have a list of concepts and the corresponding translations in all considered languages. Moreover, each reference language is assigned to one of c language clusters. For each of the concepts, the algorithm computes the distance between each pair of translations. Based on these distances, it constructs a weighted directed graph, where every vertex represents a language. After, it solves a graph diffusion equation with a Dirichlet boundary condition, where the unknown is a map from the vertex set to ℝc. The resulting coordinates are values from the interval [0,1] and they can be interpreted as probabilities of belonging to each of the clusters or as a lexical similarity distribution with respect to the reference clusters. The distances between translations are calculated using phonetic transcriptions and a modification of the Damerau-Levenshtein distance. The algorithm can be useful in analyzing relationships between languages spoken in multilingual territories with a lot of mutual influences. We demonstrate this by presenting a case study regarding various European languages.