# On node ranking in graphs

arXiv: https://arxiv.org/abs/2107.09487 · HTML: https://mlsanity.com/paper/2107.09487/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Jul 2021.

## Built on by (most cited first, 1 of 1)

- [A Network Science Perspective on Evaluating Deep Graph Generative Models](https://mlsanity.com/paper/2609.01015/) (2026)

## Abstract (arXiv)

The ranking of nodes in a network according to their ``importance'' is a classic problem that has attracted the interest of different scientific communities in the last decades. The current COVID-19 pandemic has recently rejuvenated the interest in this problem, as it is related to the selection of which individuals should be tested in a population of asymptomatic individuals, or which individuals should be vaccinated first. Motivated by the COVID-19 spreading dynamics, in this paper we review the most popular methods for node ranking in undirected unweighted graphs, and compare their performance in a benchmark realistic network, that takes into account the community-based structure of society. Also, we generalize a classic benchmark network originally proposed by Newman for ranking nodes in unweighted graphs, to show how ranks change in the weighted case.