Ranking nodes in growing networks: When PageRank fails.

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

SUBMITTER: Mariani MS 

PROVIDER: S-EPMC4639772 | biostudies-literature | 2015 Nov

REPOSITORIES: biostudies-literature