Online estimating the k central nodes of a network

A well known way to find the most central nodes in a network consists of coupling random walk sampling (or one of its variants) with a method to identify the most central nodes in the subgraph induced by the samples. Although it is commonly assumed that degree information is collected during the sampling step, in previous works this information has not been used at the identification step [10], [18]. In this paper, we showed that using degree information at the identification step in a very naive way, namely setting the degree as an alias to other centrality metrics, yields promising results.