Dynamic PageRank using Evolving Teleportation

TL;DR

This paper introduces an evolving teleportation approach to PageRank that dynamically adjusts node importance based on external interest, effectively capturing fluctuations in network significance over time.

Contribution

It presents a novel dynamic PageRank model incorporating external interest, generalizing traditional PageRank and demonstrating its effectiveness on real-world social and web networks.

Findings

Effective in modeling external interest fluctuations

Converges to traditional PageRank when external interest stabilizes

Validated on Wikipedia and Twitter networks

Abstract

The importance of nodes in a network constantly fluctuates based on changes in the network structure as well as changes in external interest. We propose an evolving teleportation adaptation of the PageRank method to capture how changes in external interest influence the importance of a node. This framework seamlessly generalizes PageRank because the importance of a node will converge to the PageRank values if the external influence stops changing. We demonstrate the effectiveness of the evolving teleportation on the Wikipedia graph and the Twitter social network. The external interest is given by the number of hourly visitors to each page and the number of monthly tweets for each user.