Maintenance
This commit is contained in:
hyginn
parent
0e71fc337e
commit
02e20ba812
@ -1,11 +1,5 @@
|
||||
# tocID <- "FND-MAT-Graphs_and_networks.R"
|
||||
#
|
||||
# ---------------------------------------------------------------------------- #
|
||||
# PATIENCE ... #
|
||||
# Do not yet work wih this code. Updates in progress. Thank you. #
|
||||
# boris.steipe@utoronto.ca #
|
||||
# ---------------------------------------------------------------------------- #
|
||||
#
|
||||
# Purpose: A Bioinformatics Course:
|
||||
# R code accompanying the FND-MAT-Graphs_and_networks unit.
|
||||
#
|
||||
@ -22,7 +16,7 @@
|
||||
# 0.1 First code copied from 2016 material.
|
||||
#
|
||||
#
|
||||
# TODO:
|
||||
# TODO: Add some explicit tasks and sample solutions.
|
||||
#
|
||||
#
|
||||
# == DO NOT SIMPLY source() THIS FILE! =======================================
|
||||
@ -35,30 +29,29 @@
|
||||
|
||||
|
||||
#TOC> ==========================================================================
|
||||
#TOC>
|
||||
#TOC> Section Title Line
|
||||
#TOC> ------------------------------------------------------------
|
||||
#TOC> 1 Review 50
|
||||
#TOC> 2 DEGREE DISTRIBUTIONS 204
|
||||
#TOC> 2.1 Random graph 210
|
||||
#TOC> 2.2 scale-free graph (Barabasi-Albert) 258
|
||||
#TOC> 2.3 Random geometric graph 323
|
||||
#TOC> 3 A CLOSER LOOK AT THE igraph PACKAGE 445
|
||||
#TOC> 3.1 Basics 448
|
||||
#TOC> 3.2 Components 520
|
||||
#TOC> 4 RANDOM GRAPHS AND GRAPH METRICS 539
|
||||
#TOC> 4.1 Diameter 576
|
||||
#TOC> 5 GRAPH CLUSTERING 645
|
||||
#TOC>
|
||||
#TOC>
|
||||
#TOC> Section Title Line
|
||||
#TOC> --------------------------------------------------------------
|
||||
#TOC> 1 REVIEW 50
|
||||
#TOC> 2 DEGREE DISTRIBUTIONS 206
|
||||
#TOC> 2.1 Random graph 212
|
||||
#TOC> 2.2 scale-free graph (Barabasi-Albert) 260
|
||||
#TOC> 2.3 Random geometric graph 326
|
||||
#TOC> 3 A CLOSER LOOK AT THE igraph:: PACKAGE 448
|
||||
#TOC> 3.1 Basics 451
|
||||
#TOC> 3.2 Components 523
|
||||
#TOC> 4 RANDOM GRAPHS AND GRAPH METRICS 542
|
||||
#TOC> 4.1 Diameter 579
|
||||
#TOC> 5 GRAPH CLUSTERING 648
|
||||
#TOC>
|
||||
#TOC> ==========================================================================
|
||||
|
||||
|
||||
# = 1 Review ==============================================================
|
||||
# = 1 REVIEW ==============================================================
|
||||
|
||||
# This tutorial covers basic concepts of graph theory and analysis in R. Make
|
||||
# sure you have pulled the latest version of the project from the GitHub
|
||||
# repository, and that you have typed init() to load some utility functions and
|
||||
# data.
|
||||
# repository.
|
||||
|
||||
# Let's explore some of the basic ideas of graph theory by starting with a small
|
||||
# random graph.
|
||||
@ -82,7 +75,7 @@ makeRandomGenenames <- function(N) {
|
||||
N <- 20
|
||||
|
||||
set.seed(112358) # set RNG seed for repeatable randomness
|
||||
(Nnames <- makeRandomGenenames(N))
|
||||
( Nnames <- makeRandomGenenames(N) )
|
||||
set.seed(NULL) # reset the RNG
|
||||
|
||||
# One way to represent graphs in a computer is as an "adjacency matrix". In this
|
||||
@ -90,11 +83,11 @@ set.seed(NULL) # reset the RNG
|
||||
# intersection of a row and column contains a value/TRUE if there is an edge,
|
||||
# 0/FALSE otherwise.
|
||||
|
||||
# Let's create an adjacency matrix for random graph: let's say a pair of nodes
|
||||
# has probability p <- 0.1 to have an edge, and our graph is symmetric , i.e. it
|
||||
# is an undirected graph, and it has neither self-edges, i.e. loops, nor
|
||||
# multiple edges between the same nodes, i.e. it is a "simple" graph. We use our
|
||||
# the Nnames vector as node labels.
|
||||
# Let's create an adjacency matrix for our random graph: let's say a pair
|
||||
# of nodes has probability p <- 0.09 to have an edge, and our graph is
|
||||
# symmetric , i.e. it is an undirected graph, and it has neither self-edges,
|
||||
# i.e. loops, nor multiple edges between the same nodes, i.e. it
|
||||
# is a "simple" graph. We use the Nnames vector as node labels.
|
||||
|
||||
makeRandomAM <- function(nam, p = 0.1) {
|
||||
# Make a random adjacency matrix for a set of nodes with edge probability p
|
||||
@ -106,11 +99,11 @@ makeRandomAM <- function(nam, p = 0.1) {
|
||||
#
|
||||
|
||||
N <- length(nam)
|
||||
AM <- matrix(numeric(N * N), ncol = N) # The adjacency matrix
|
||||
AM <- matrix(numeric(N * N), ncol = N) # Adjacency matrix, pre-filled with 0
|
||||
rownames(AM) <- nam
|
||||
colnames(AM) <- nam
|
||||
for (iRow in 1:(N-1)) { # Note how we make sure iRow != iCol - this prevents
|
||||
# loops
|
||||
# loops (self-edges)
|
||||
for (iCol in (iRow+1):N) {
|
||||
if (runif(1) < p) { # runif() creates uniform random numbers
|
||||
# between 0 and 1. The expression is TRUE with
|
||||
@ -123,13 +116,13 @@ makeRandomAM <- function(nam, p = 0.1) {
|
||||
}
|
||||
|
||||
set.seed(112358) # set RNG seed for repeatable randomness
|
||||
(myRandAM <- makeRandomAM(Nnames, p = 0.09))
|
||||
( myRandAM <- makeRandomAM(Nnames, p = 0.09) )
|
||||
set.seed(NULL) # reset the RNG
|
||||
|
||||
|
||||
# Listing the matrix is not very informative - we should plot this graph. The
|
||||
# standard package for work with graphs in r is "igraph". We'll go into more
|
||||
# details of the igraph package a bit later, for now we just use it to plot:
|
||||
# standard package for work with graphs in r is "igraph::". We'll go into more
|
||||
# details of the igraph:: package a bit later, for now we just use it to plot:
|
||||
|
||||
if (! requireNamespace("igraph", quietly = TRUE)) {
|
||||
install.packages("igraph")
|
||||
@ -149,7 +142,7 @@ myGxy <- igraph::layout_with_graphopt(myG,
|
||||
set.seed(NULL) # reset the RNG
|
||||
|
||||
|
||||
# The igraph package adds its own function to the collection of plot()
|
||||
# The igraph:: package adds its own function to the collection of plot()
|
||||
# functions; R makes the selection which plot function to use based on the class
|
||||
# of the object that we request to plot. This plot function has parameters
|
||||
# layout - the x,y coordinates of the nodes;
|
||||
@ -160,14 +153,14 @@ set.seed(NULL) # reset the RNG
|
||||
# See ?igraph.plotting for the complete list of parameters
|
||||
|
||||
|
||||
oPar <- par(mar= rep(0,4)) # Turn margins off
|
||||
oPar <- par(mar= c(0, 0, 0, 0)) # Turn margins off
|
||||
plot(myG,
|
||||
layout = myGxy,
|
||||
rescale = FALSE,
|
||||
xlim = c(min(myGxy[,1]) * 0.99, max(myGxy[,1]) * 1.01),
|
||||
ylim = c(min(myGxy[,2]) * 0.99, max(myGxy[,2]) * 1.01),
|
||||
vertex.color=heat.colors(max(igraph::degree(myG)+1))[igraph::degree(myG)+1],
|
||||
vertex.size = 1600 + (300 * igraph::degree(myG)),
|
||||
vertex.size = 1000 + (900 * igraph::degree(myG)),
|
||||
vertex.label = sprintf("%s(%i)", names(igraph::V(myG)), igraph::degree(myG)),
|
||||
vertex.label.family = "sans",
|
||||
vertex.label.cex = 0.7)
|
||||
@ -185,7 +178,7 @@ sum(myRandAM)
|
||||
# What about the degree distribution? We can get that simply by summing over the
|
||||
# rows and summing over the columns and adding the two vectors.
|
||||
|
||||
rowSums(myRandAM) + colSums(myRandAM) # check this against the plot!
|
||||
rowSums(myRandAM) + colSums(myRandAM) # check this against the plot!
|
||||
|
||||
# The function degree() gives the same values
|
||||
igraph::degree(myG)
|
||||
@ -202,10 +195,13 @@ axis(side = 1, at = 0:7)
|
||||
# quite well, automatically. But for this purpose I want the columns of the
|
||||
# histogram to represent exactly one node-degree difference.
|
||||
|
||||
# A degree distribution is actually quite an important descriptor of graphs,
|
||||
# since it is very sensitive to the generating mechanism. For biological
|
||||
# A degree distribution is actually a very informative descriptor of graphs,
|
||||
# since it is very sensitive to the generating mechanism. For biological
|
||||
# networks, that is one of the key questions we are interested in: how was the
|
||||
# network formed?
|
||||
# network formed? Asking about the mechanism, the way the interactions
|
||||
# support function and shape the fitness landscape in which the cell evolves,
|
||||
# connects our mathematical abstraction with biological insight.
|
||||
|
||||
|
||||
# = 2 DEGREE DISTRIBUTIONS ================================================
|
||||
|
||||
@ -225,14 +221,14 @@ myG200 <- igraph::graph_from_adjacency_matrix(my200AM, mode = "undirected")
|
||||
myGxy <- igraph::layout_with_graphopt(myG200, charge=0.0001) # calculate layout
|
||||
# coordinates
|
||||
|
||||
oPar <- par(mar= rep(0,4)) # Turn margins off, save graphics state
|
||||
oPar <- par(mar= c(0, 0, 0, 0)) # Turn margins off, save graphics state
|
||||
plot(myG200,
|
||||
layout = myGxy,
|
||||
rescale = FALSE,
|
||||
xlim = c(min(myGxy[,1]) * 0.99, max(myGxy[,1]) * 1.01),
|
||||
ylim = c(min(myGxy[,2]) * 0.99, max(myGxy[,2]) * 1.01),
|
||||
vertex.color=heat.colors(max(igraph::degree(myG200)+1))[igraph::degree(myG200)+1],
|
||||
vertex.size = 150 + (60 * igraph::degree(myG200)),
|
||||
vertex.size = 150 + (70 * igraph::degree(myG200)),
|
||||
vertex.label = NA)
|
||||
par(oPar) # restore graphics state
|
||||
|
||||
@ -251,7 +247,7 @@ axis(side = 1, at = 0:10)
|
||||
# Note the pronounced peak of this distribution: this is not "scale-free".
|
||||
# Here is the log-log plot of frequency vs. degree-rank ...
|
||||
|
||||
(freqRank <- table(dg))
|
||||
( freqRank <- table(dg) )
|
||||
plot(log10(as.numeric(names(freqRank)) + 1),
|
||||
log10(as.numeric(freqRank)), type = "b",
|
||||
pch = 21, bg = "#A5F5CC",
|
||||
@ -264,9 +260,9 @@ plot(log10(as.numeric(names(freqRank)) + 1),
|
||||
# == 2.2 scale-free graph (Barabasi-Albert) ================================
|
||||
|
||||
# What does one of those intriguing "scale-free" distributions look like? The
|
||||
# iGraph package has a function to make random graphs according to the
|
||||
# iGraph:: package has a function to make random graphs according to the
|
||||
# Barabasi-Albert model of scale-free graphs. It is: sample_pa(), where pa
|
||||
# stands for "preferential attachment". Preferential attachment is one type of
|
||||
# stands for "preferential attachment". Preferential attachment is _one_ type of
|
||||
# process that will yield scale-free distributions.
|
||||
|
||||
N <- 200
|
||||
@ -277,7 +273,7 @@ set.seed(NULL) # reset the RNG
|
||||
|
||||
GBAxy <- igraph::layout_with_graphopt(GBA, charge=0.0001)
|
||||
|
||||
oPar <- par(mar= rep(0,4)) # Turn margins off, save graphics state
|
||||
oPar <- par(mar= c(0, 0, 0, 0)) # Turn margins off, save graphics state
|
||||
plot(GBA,
|
||||
layout = GBAxy,
|
||||
rescale = FALSE,
|
||||
@ -296,7 +292,7 @@ par(oPar) # restore graphics state
|
||||
# singletons.
|
||||
|
||||
# What's the degree distribution of this graph?
|
||||
(dg <- igraph::degree(GBA))
|
||||
( dg <- igraph::degree(GBA) )
|
||||
brk <- seq(min(dg)-0.5, max(dg)+0.5, by=1)
|
||||
hist(dg, breaks=brk, col="#DCF5B5",
|
||||
xlim = c(0,max(dg)+1), xaxt = "n",
|
||||
@ -314,9 +310,10 @@ plot(log10(as.numeric(names(freqRank)) + 1),
|
||||
main = "200 nodes in a preferential-attachment network")
|
||||
|
||||
# Sort-of linear, but many of the higher ranked nodes have a frequency of only
|
||||
# one. That behaviour smooths out in larger graphs:
|
||||
# one. That behaviour smooths out in larger graphs - let's try 100000 nodes:
|
||||
#
|
||||
X <- igraph::sample_pa(1e5, power = 0.8, directed = FALSE) # 100,000 nodes
|
||||
N <- 10000
|
||||
X <- igraph::sample_pa(N, power = 0.8, directed = FALSE) # 100,000 nodes
|
||||
freqRank <- table(igraph::degree(X))
|
||||
plot(log10(as.numeric(names(freqRank)) + 1),
|
||||
log10(as.numeric(freqRank)), type = "b",
|
||||
@ -336,16 +333,16 @@ rm(X)
|
||||
# probability for two nodes to have an edge to be a function of their Euclidian
|
||||
# distance in the box.
|
||||
|
||||
# Here is a function that makes an adjacency matrix for such graphs. iGraph has
|
||||
# a similar function, sample_grg(), which connects nodes that are closer than a
|
||||
# cutoff, the function I give you below is a bit more interesting since it
|
||||
# creates edges according to a probability that is determined by a generalized
|
||||
# logistic function of the distance. This sigmoidal function gives a smooth
|
||||
# cutoff and creates more "natural" graphs. Otherwise, the function is very
|
||||
# similar to the random graph function, except that we output the "coordinates"
|
||||
# of the nodes together with the adjacency matrix which we then use for the
|
||||
# layout. list() FTW.
|
||||
#
|
||||
# Here is a function that makes an adjacency matrix for such graphs. iGraph::
|
||||
# has a similar function, sample_grg(), which connects nodes that are closer
|
||||
# than a cutoff, the function I give you below is a bit more interesting since
|
||||
# it creates edges according to a probability that is determined by a
|
||||
# generalized logistic function of the distance. This sigmoidal function gives
|
||||
# a smooth cutoff and creates more "natural" graphs. Otherwise, the function is
|
||||
# very similar to the random graph function, except that we output the
|
||||
# "coordinates" of the nodes together with the adjacency matrix which we then
|
||||
# use for the layout. list() FTW.
|
||||
|
||||
|
||||
makeRandomGeometricAM <- function(nam, B = 25, Q = 0.001, t = 0.6) {
|
||||
# Make an adjacency matrix for an undirected random geometric graph from
|
||||
@ -400,7 +397,7 @@ makeRandomGeometricAM <- function(nam, B = 25, Q = 0.001, t = 0.6) {
|
||||
# }
|
||||
#
|
||||
# ... and this code plots p-values over the distances we could encouter between
|
||||
# our nodes: from 0 to sqrt(2) i.e. the diagonal of the unit sqaure in which we
|
||||
# our nodes: from 0 to sqrt(2) i.e. the diagonal of the unit square in which we
|
||||
# will place our nodes.
|
||||
# x <- seq(0, sqrt(2), length.out = 50)
|
||||
# plot(x, genLog(x), type="l", col="#AA0000", ylim = c(0, 1),
|
||||
@ -415,7 +412,7 @@ set.seed(NULL) # reset the RNG
|
||||
|
||||
myGRG <- igraph::graph_from_adjacency_matrix(rGAM$mat, mode = "undirected")
|
||||
|
||||
oPar <- par(mar= rep(0,4)) # Turn margins off
|
||||
oPar <- par(mar= c(0, 0, 0, 0)) # Turn margins off
|
||||
plot(myGRG,
|
||||
layout = cbind(rGAM$x, rGAM$y), # use our node coordinates for layout,
|
||||
rescale = FALSE,
|
||||
@ -448,12 +445,12 @@ plot(log10(as.numeric(names(freqRank)) + 1),
|
||||
|
||||
|
||||
|
||||
# = 3 A CLOSER LOOK AT THE igraph PACKAGE =================================
|
||||
# = 3 A CLOSER LOOK AT THE igraph:: PACKAGE ===============================
|
||||
|
||||
|
||||
# == 3.1 Basics ============================================================
|
||||
|
||||
# The basic object of the igraph package is a graph object. Let's explore the
|
||||
# The basic object of the igraph:: package is a graph object. Let's explore the
|
||||
# first graph some more, the one we built with our random gene names:
|
||||
summary(myG)
|
||||
|
||||
@ -464,7 +461,7 @@ summary(myG)
|
||||
mode(myG)
|
||||
class(myG)
|
||||
|
||||
# This means an igraph graph object is a special list object; it is opaque in
|
||||
# This means an igraph:: graph object is a special list object; it is opaque in
|
||||
# the sense that a user is never expected to modify its components directly, but
|
||||
# through a variety of helper functions which the package provides. There are
|
||||
# many ways to construct graphs - from adjacency matrices, as we have just done,
|
||||
@ -494,12 +491,12 @@ plot(myG) # this is the result of default plot parameters
|
||||
# See ?plot.igraph for details."
|
||||
|
||||
# Plot with some customizing parameters
|
||||
oPar <- par(mar= rep(0,4)) # Turn margins off
|
||||
oPar <- par(mar= c(0, 0, 0, 0)) # Turn margins off
|
||||
plot(myG,
|
||||
layout = igraph::layout_with_fr(myG),
|
||||
vertex.color=heat.colors(max(igraph::degree(myG)+1))[igraph::degree(myG)+1],
|
||||
vertex.size = 9 + (2 * igraph::degree(myG)),
|
||||
vertex.label.cex = 0.5 + (0.05 * igraph::degree(myG)),
|
||||
vertex.size = 12 + (5 * igraph::degree(myG)),
|
||||
vertex.label.cex = 0.4 + (0.15 * igraph::degree(myG)),
|
||||
edge.width = 2,
|
||||
vertex.label = igraph::V(myG)$name,
|
||||
vertex.label.family = "sans",
|
||||
@ -507,25 +504,25 @@ plot(myG,
|
||||
par(oPar)
|
||||
|
||||
# ... or with a different layout:
|
||||
oPar <- par(mar= rep(0,4)) # Turn margins off
|
||||
oPar <- par(mar= c(0, 0, 0, 0)) # Turn margins off
|
||||
plot(myG,
|
||||
layout = igraph::layout_in_circle(myG),
|
||||
vertex.color=heat.colors(max(igraph::degree(myG)+1))[igraph::degree(myG)+1],
|
||||
vertex.size = 9 + (2 * igraph::degree(myG)),
|
||||
vertex.label.cex = 0.5 + (0.05 * igraph::degree(myG)),
|
||||
vertex.size = 12 + (5 * igraph::degree(myG)),
|
||||
vertex.label.cex = 0.4 + (0.15 * igraph::degree(myG)),
|
||||
edge.width = 2,
|
||||
vertex.label = igraph::V(myG)$name,
|
||||
vertex.label.family = "sans",
|
||||
vertex.label.cex = 0.9)
|
||||
par(oPar)
|
||||
|
||||
# igraph has a large number of graph-layout functions: see
|
||||
# igraph:: has a large number of graph-layout functions: see
|
||||
# ?layout_ and try them all.
|
||||
|
||||
|
||||
# == 3.2 Components ========================================================
|
||||
|
||||
# The igraph function components() tells us whether there are components of the
|
||||
# The function igrap::components() tells us whether there are components of the
|
||||
# graph in which there is no path to other components.
|
||||
igraph::components(myG)
|
||||
|
||||
@ -548,7 +545,7 @@ names(c1)
|
||||
# Let's explore some of the more interesting, topological graph measures. We
|
||||
# start by building a somewhat bigger graph. We aren't quite sure whether
|
||||
# biological graphs are small-world, or random-geometric, or
|
||||
# preferential-attachment ... but igraph has ways to simulate the basic ones
|
||||
# preferential-attachment ... but igraph:: has ways to simulate the basic ones
|
||||
# (and we could easily simulate our own). Look at the following help pages:
|
||||
|
||||
?igraph::sample_gnm # see also sample_gnp for the Erdös-Rényi models
|
||||
@ -573,10 +570,10 @@ plot(myG,
|
||||
xlim = c(min(myGxy[,1]) * 0.99, max(myGxy[,1]) * 1.01),
|
||||
ylim = c(min(myGxy[,2]) * 0.99, max(myGxy[,2]) * 1.01),
|
||||
vertex.color=heat.colors(max(igraph::degree(myG)+1))[igraph::degree(myG)+1],
|
||||
vertex.size = 20 + (10 * igraph::degree(myG)),
|
||||
vertex.size = 40 + (15 * igraph::degree(myG)),
|
||||
vertex.label.cex = 0.4 + (0.15 * igraph::degree(myG)),
|
||||
vertex.label = igraph::V(myG)$name,
|
||||
vertex.label.family = "sans",
|
||||
vertex.label.cex = 0.8)
|
||||
vertex.label.family = "sans")
|
||||
par(oPar) # restore graphics state
|
||||
|
||||
# == 4.1 Diameter ==========================================================
|
||||
@ -600,17 +597,17 @@ bC <- igraph::centr_betw(myG) # calculate betweenness centrality
|
||||
nodeBetw <- bC$res
|
||||
nodeBetw <- round(log(nodeBetw +1)) + 1
|
||||
|
||||
oPar <- par(mar= rep(0,4)) # Turn margins off
|
||||
oPar <- par(mar= c(0, 0, 0, 0)) # Turn margins off
|
||||
plot(myG,
|
||||
layout = myGxy,
|
||||
rescale = FALSE,
|
||||
xlim = c(min(myGxy[,1]) * 0.99, max(myGxy[,1]) * 1.01),
|
||||
ylim = c(min(myGxy[,2]) * 0.99, max(myGxy[,2]) * 1.01),
|
||||
vertex.color=heat.colors(max(nodeBetw))[nodeBetw],
|
||||
vertex.size = 20 + (10 * igraph::degree(myG)),
|
||||
vertex.color=heat.colors(max(igraph::degree(myG)+1))[igraph::degree(myG)+1],
|
||||
vertex.size = 40 + (15 * igraph::degree(myG)),
|
||||
vertex.label.cex = 0.4 + (0.15 * igraph::degree(myG)),
|
||||
vertex.label = igraph::V(myG)$name,
|
||||
vertex.label.family = "sans",
|
||||
vertex.label.cex = 0.7)
|
||||
vertex.label.family = "sans")
|
||||
par(oPar)
|
||||
|
||||
# Note that the betweenness - the number of shortest paths that pass through a
|
||||
@ -629,7 +626,7 @@ nodeBetw <- bCmyGRG$res
|
||||
nodeBetw <- round((log(nodeBetw +1))^2.5) + 1
|
||||
|
||||
# colours and size proportional to betweenness
|
||||
oPar <- par(mar= rep(0,4)) # Turn margins off
|
||||
oPar <- par(mar= c(0, 0, 0, 0)) # Turn margins off
|
||||
plot(myGRG,
|
||||
layout = cbind(rGAM$x, rGAM$y), # use our node coordinates for layout,
|
||||
rescale = FALSE,
|
||||
@ -677,18 +674,18 @@ commColors <- rep("#f1eef6", max(igraph::membership(myGRGclusters)))
|
||||
commColors[1:5] <- c("#980043", "#dd1c77", "#df65b0", "#c994c7", "#d4b9da")
|
||||
|
||||
|
||||
oPar <- par(mar= rep(0,4)) # Turn margins off
|
||||
oPar <- par(mar= c(0, 0, 0, 0)) # Turn margins off
|
||||
plot(myGRG,
|
||||
layout = cbind(rGAM$x, rGAM$y),
|
||||
rescale = FALSE,
|
||||
xlim = c(min(rGAM$x) * 0.9, max(rGAM$x) * 1.1),
|
||||
ylim = c(min(rGAM$y) * 0.9, max(rGAM$y) * 1.1),
|
||||
vertex.color=commColors[igraph::membership(myGRGclusters)],
|
||||
vertex.size = 0.1 + (0.1 * igraph::degree(myGRG)),
|
||||
vertex.size = 0.3 + (0.4 * igraph::degree(myGRG)),
|
||||
vertex.label = NA)
|
||||
par(oPar)
|
||||
|
||||
|
||||
# That's all.
|
||||
|
||||
|
||||
# [END]
|
||||
|
||||
Loading…
Reference in New Issue
Block a user