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