Abstract:
: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.
journal_name
Sci Repjournal_title
Scientific reportsauthors
Mariani MS,Medo M,Zhang YCdoi
10.1038/srep16181subject
Has Abstractpub_date
2015-11-10 00:00:00pages
16181issn
2045-2322pii
srep16181journal_volume
5pub_type
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