PageRank in Undirected Random Graphs
  PageRank has numerous applications in information retrieval, reputation
systems, machine learning, and graph partitioning. In this paper, we study
PageRank in undirected random graphs with an expansion property. The Chung-Lu
random graph is an example of such a graph. We show that in the limit, as the
size of the graph goes to infinity, PageR- ank can be approximated by a mixture
of the restart distribution and the vertex degree distribution. We also extend
the result to Stochastic Block Model (SBM) graphs, where we show that there is
a correction term that depends on the community partitioning.
