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Surfing the links between Wikipedia articles constitutes a valuable way to acquire new knowledge related to a topic by exploring its connections to other pages. In this sense,Personalized PageRank is a well-known option to make sense of the graph of links between pages and identify the most relevant articles with respect to a given one; its performance, however, is hindered by pages with high indegree that function as hubs and obtain high scores regardless of the starting point. In this work, we presentCycleRank , a novel algorithm based on cyclic paths aimed at finding the most relevant nodes related to a topic. To compare the results ofCycleRank with those ofPersonalized PageRank and other algorithms derived from it, we perform three experiments based on different ground truths. We find thatCycleRank aligns better with readers’ behaviour as it ranks in higher positions the articles corresponding to links that receive more clicks; it tends to identify in higher position related articles highlighted by editors in ‘See also’ sections; and it is more robust to global hubs of the network having high indegree. Finally, we show that computingCycleRank is two orders of magnitude faster than computing the other baselines.

CycleRank , or there and back again: personalized relevance scores from cyclic paths on directed graphs