  Online network crawling tasks require a lot of efforts for the researchers to
collect the data. One of them is identification of important nodes, which has
many applications starting from viral marketing to the prevention of disease
spread. Various crawling algorithms has been suggested but their efficiency is
not studied well. In this paper we compared six known crawlers on the task of
collecting the fraction of the most influential nodes of graph.
  We analyzed crawlers behavior for four measures of node influence: node
degree, k-coreness, betweenness centrality, and eccentricity. The experiments
confirmed that greedy methods perform the best in many settings, but the cases
exist when they are very inefficient.
