Dynamic PageRank using Evolving Teleportation
This work addresses dynamic ranking for networks like Wikipedia and Twitter, but it is incremental as it builds on the established PageRank method.
The authors tackled the problem of capturing fluctuating node importance in networks due to changes in external interest by proposing an evolving teleportation adaptation of PageRank, demonstrating effectiveness on Wikipedia and Twitter graphs with metrics like hourly visitors and monthly tweets.
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.