  Connections among entities are everywhere. From social media interactions to
web page hyperlinks, networks are frequently used to represent such complex
systems. Node ranking is a fundamental task that provides the strategy to
identify central entities according to multiple criteria. Popular node ranking
metrics include degree, closeness or betweenness centralities, as well as HITS
authority or PageRank. In this work, we propose a novel node ranking metric,
where we combine PageRank and the idea of node fatigue, in order to model a
random explorer who wants to optimize coverage - it gets fatigued and avoids
previously visited nodes. We formalize and exemplify the computation of
Fatigued PageRank, evaluating it as a node ranking metric, as well as
query-independent evidence in ad hoc document retrieval. Based on the Simple
English Wikipedia link graph with clickstream transitions from the English
Wikipedia, we find that Fatigued PageRank is able to surpass both indegree and
HITS authority, but only for the top ranking nodes. On the other hand, based on
the TREC Washington Post Corpus, we were unable to outperform the BM25
baseline, obtaining similar performance for all graph-based metrics, except for
indegree, which lowered GMAP and MAP, but increased NDCG@10 and P@10.
