Ranking nodes in growing networks: When PageRank fails
#MMPMID26553630
Mariani MS
; Medo M
; Zhang YC
Sci Rep
2015[Nov]; 5
(?): 16181
PMID26553630
show ga
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.
free
free
free
