Ranking nodes in growing networks: When PageRank fails

TL;DR

This paper demonstrates that PageRank, a widely used ranking algorithm, often fails to identify the most valuable nodes in growing networks due to temporal effects, highlighting the need for time-dependent ranking methods.

Contribution

The study reveals the limitations of PageRank in dynamic networks and proposes that temporal linking patterns should be incorporated into ranking algorithms.

Findings

PageRank fails to identify key nodes in growing networks.

Temporal effects significantly impact ranking accuracy.

Real data supports the model-based findings.

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.