# Traditional PageRank versus Network Capacity Bound

## TL;DR

This paper compares traditional PageRank with network capacity bounds, focusing on the differences when the random jump is uniformly distributed versus personalized, and explores implications for graph clustering.

## Contribution

It extends previous work on personalized random walks to analyze the effects of uniform jump distributions on PageRank and network capacity bounds.

## Key findings

- Uniform jump distribution impacts PageRank calculations
- Differences between personalized and uniform jumps are significant
- Provides insights into graph clustering methods

## Abstract

In a former paper we simplified the proof of a theorem on personalized random walk that is fundamental to graph nodes clustering and generalized it to bipartite graphs for a specific case where the proobability of random jump was proprtional to the number of links of "personally prefereed" nodes. In this paper we turn to the more complex issue of graphs in which the random jump follows uniform distribution.