PageRank: Compute a PageRank vector

In Selbosh/scrooge: Statistical modelling of heterogeneous citation networks

Description

Given a (weighted) adjacency matrix, compute the PageRank: the the stationary distribution of a random walk around the graph.

Usage

PageRank(C, alpha = 0.85, sort = FALSE)

Arguments

C	a square matrix
alpha	a damping factor
sort	logical. Reorder the indices in descending order of PageRank

Details

PageRank is an eigenvector centrality metric, equivalent to Pinski & Narin's influence weight (1976) with the addition of a damping factor alpha, which simulates a random surfer traversing the graph and teleporting at any time with probability 1 - alpha. The effect of the damping factor is to smooth out any disconnected components or transient portions of the network.

In bibliometrics, PageRank has also been implemented as the Eigenfactor Metric and as the SCImago Journal Rank.

By default, C[i,j] refers to the directed edge that points from column j to row i. Use t(C) if you want edges directed from rows to columns instead.

Value

A PageRank vector, scaled to sum to one

References

Pinski, G., & Narin, F. (1976). Citation influence for journal aggregates of scientific publications: Theory, with application to the literature of physics. Information Processing & Management, 12(5), 297–312.

Page, L., Brin, S., Motwani, R., & Winograd, T. (1999). The PageRank citation ranking: bringing order to the web. Technical Report, Stanford InfoLab.

See Also

Other network centrality estimators: BTscores, BradleyTerry, ILSR, Scroogefactor

Examples

PageRank(citations)

# Scroogefactor, an estimator for the Bradley-Terry model
PageRank(citations)/colSums(citations)

Selbosh/scrooge documentation built on May 5, 2019, 8 p.m.