  PageRank is arguably the most popular ranking algorithm which is being
applied in real systems ranging from information to biological and
infrastructure networks. Despite its outstanding popularity and broad use in
different areas of science, the relation between the algorithm's efficacy and
properties of the network on which it acts has not yet been fully understood.
We study here PageRank's performance on a network model supported by real data,
and show that realistic temporal effects make PageRank fail in individuating
the most valuable nodes for a broad range of model parameters. Results on real
data are in qualitative agreement with our model-based findings. This failure
of PageRank reveals that the static approach to information filtering is
inappropriate for a broad class of growing systems, and suggest that
time-dependent algorithms that are based on the temporal linking patterns of
these systems are needed to better rank the nodes.
