PageRank in undirected random graphs

TL;DR

This paper analyzes the behavior of PageRank in large undirected random graphs with expansion properties, showing it converges to a mixture of restart and degree distributions.

Contribution

It provides a theoretical characterization of PageRank in undirected random graphs, especially Chung-Lu graphs, as the graph size tends to infinity.

Findings

PageRank converges to a mixture of restart and degree distributions in large graphs

The analysis applies to graphs with expansion properties like Chung-Lu graphs

As graph size increases, PageRank behavior becomes predictable and analytically describable

Abstract

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 expansion property. The Chung-Lu random graph representsan example of such graphs. We show that in the limit, as the size of the graph goes to infinity, PageRank can be represented by a mixture of the restart distribution and the vertex degree distribution.