inst/doc/indirect_relations.R

In netrankr: Analyzing Partial Rankings in Networks

## ----setup, warning=FALSE,message=FALSE---------------------------------------
library(netrankr)
library(igraph)

## ----indirstandard------------------------------------------------------------
data("dbces11")
g <- dbces11

# adjacency
A <- indirect_relations(g, type = "adjacency")
# shortest path distances
D <- indirect_relations(g, type = "dist_sp")
# dyadic dependencies (as used in betweenness centrality)
B <- indirect_relations(g, type = "depend_sp")
# resistance distance (as used in information centrality)
R <- indirect_relations(g, type = "dist_resist")
# Logarithmic forest distance (parametrized family of distances)
LF <- indirect_relations(g, type = "dist_lf", lfparam = 1)
# Walk distance (parametrized family of distances)
WD <- indirect_relations(g, type = "dist_walk", dwparam = 0.001)
# Random walk distance
WD <- indirect_relations(g, type = "dist_rwalk")
# See ?indirect_relations for further options

## ----example_mat--------------------------------------------------------------
D
B

## ----indirwalks---------------------------------------------------------------
# count the limit proportion of walks (used for eigenvector centrality)
W <- indirect_relations(g, type = "walks", FUN = walks_limit_prop)
# count the number of walks of arbitrary length between nodes, weighted by
# the inverse factorial of their length (used for subgraph centrality)
S <- indirect_relations(g, type = "walks", FUN = walks_exp)

## ----indirparam---------------------------------------------------------------
# Calculate dist(s,t)^-alpha
D <- indirect_relations(g, type = "dist_sp", FUN = dist_dpow, alpha = 2)

## ----own_func-----------------------------------------------------------------
dist_integration <- function(x) {
    x <- 1 - (x - 1) / max(x)
}
D <- indirect_relations(g, type = "dist_sp", FUN = dist_integration)