NOTE: We issued a correction when we discovered the R function as used in the paper was not directly implementing the measure as defined by the equations in the paper. The code provided here will replicate the revised and republished manuscript in Socius. See the textSpan.R file at https://github.com/dustinstoltz/textSpan for the original (erroneous) function.
Dustin S. Stoltz and Marshall A. Taylor
This is the code and data necessary to reproduce the measures, graphs, and plots for Stoltz and Taylor (2019) "Textual Spanning: Finding Discursive Holes in Text Networks" in Socius. Note, again, that this code will reproduce the updated version of the paper, re-published in 2021.
The basic data structure in text analysis is an MxN matrix of documents by terms, n-grams, parts of speech, topics, and so on. The rows, therefore, represents the documents as vectors which can be compared for similarity. This is usually accomplished with cosine similarity, but there are several other ways (and our measure does not depend upon any particular measure, but the substantive interpretation of spanning will change accordingly). The result of these similarity measures is a one-mode document by document (MxM) matrix, which can easily be interpreted as a weighted adjacency matrix amendable to network metrics.
The textSpan function takes this document by document similarity matrix and outputs a document specific measure which increases when a document is similar to documents which are not also similar to each other. This is defined by the following equations:
We define proportional similarities as:
Finally, to make the measure more interpretable, we standardize the output by taking the z-score of each and inverting it such that positive values indicate more textual spanning, while negative values indicate less textual spanning:
For a more detailed exposition of the measure see the paper.
# corrected function
textSpan <- function(A, alpha=1){
diag(A) <- 0
den <- (rowSums(A != 0)) * ((rowSums(A)/
(rowSums(A != 0)))^alpha)
PS <- A/den
# --------------------
# corrected:
ePS <- PS^-1 # invert PS for division
ePS[is.infinite(ePS)] <- 0
PS2 <- ePS %*% PS # sum paths of length two
SP <- (PS + PS2)^2
# --------------------
cSP <- rowSums(SP)
cSP <- ((cSP-mean(cSP))/sd(cSP))*-1
return(cSP)
}To reproduce the examples provided in our Socius paper, follow the steps below (after loading the textSpan function above).
Download CSVs, set your working directory, and load these additional packages used in the analysis and visualization:
#install.packages("pacman")
library(pacman)
pacman::p_load(ggnetwork, ggplot2, ggpubr, igraph,
corrplot, tnet, PerformanceAnalytics,
stm, reshape2, qgraph, intergraph,
install = T)
Load simulated datasets:
sim0 <- read.csv("2_sim_ring_0.csv", stringsAsFactors=FALSE, row.names=1)
sim1 <- read.csv("2_sim_ring_b.csv", stringsAsFactors=FALSE, row.names=1)
sim2 <- read.csv("2_sim_clique.csv", stringsAsFactors=FALSE, row.names=1)
sim3 <- read.csv("2_sim_core_periph.csv", stringsAsFactors=FALSE, row.names=1)We use the CMU political blogs dataset which are provided in the stm package. Because n=13,000 in this dataset, for simplicity we randomly sample 100 blog posts which we use in the following. We offer a subset of the document by term matrix and the document by topic probability matrix, the latter of which is based on the pre-processed topic model solution provided by CMU team (the RData file can be downloaded here: http://goo.gl/VPdxlS).
Load pre-fitted topic model solution on subset of 100 randomly sampled blogposts:
tms <- as.matrix(read.csv("2_subset_topic_solution.csv",
stringsAsFactors=FALSE, row.names=1))Calculate cosine similarities between documents based on topic model solution:
cos.tms <- tcrossprod(tms / sqrt(rowSums(tms * tms)))Create tnet object to calculate Opsahl et al's weighted measures:
# simulated ring graphs
tn.sim0 <- as.tnet(sim0, type="weighted one-mode tnet")
tn.sim1 <- as.tnet(sim1, type="weighted one-mode tnet")
# topic model graph
tn.tms <- as.tnet(cos.tms, type="weighted one-mode tnet")Create iGraph object for visualization:
# simulated graphs
sim.net0 <- graph.adjacency(as.matrix(sim0), diag=F, mode="lower", weighted=T)
sim.net1 <- graph.adjacency(as.matrix(sim1), diag=F, mode="lower", weighted=T)
sim.net2 <- graph.adjacency(as.matrix(sim2), diag=F, mode="lower", weighted=T)
sim.net3 <- graph.adjacency(as.matrix(sim3), diag=F, mode="lower", weighted=T)
# topic model graph
g.tms <- graph_from_adjacency_matrix(cos.tms,
weighted = TRUE,
diag = FALSE,
add.colnames = NULL) # Textual SPANNING
# simulated graphs
# alpha set to 1.0
V(sim.net0)$span.1.0 <- textSpan(as.matrix(sim0), alpha = 1.0)
V(sim.net1)$span.1.0 <- textSpan(as.matrix(sim1), alpha = 1.0)
V(sim.net2)$span.1.0 <- textSpan(as.matrix(sim2), alpha = 1.0)
V(sim.net3)$span.1.0 <- textSpan(as.matrix(sim3), alpha = 1.0)
# topic model graph
# alpha = 1.0
V(g.tms)$span.1.0 <- textSpan(cos.tms, alpha = 1.0)
# Opsahl et al's WEIGHTED DEGREE
# simulated ring graphs
# alpha = 1.0
V(sim.net0)$deg.1.0 <- as.data.frame(degree_w(tn.sim0, alpha=1.0))$output
V(sim.net1)$deg.1.0 <- as.data.frame(degree_w(tn.sim1, alpha=1.0))$output
# topic model graph
# alpha = 1.0
V(g.tms)$deg.1.0 <- as.data.frame(degree_w(tn.tms, alpha=1.0))$output
# Opsahl et al's WEIGHTED BETWEENNESS
# alpha = 1.0
V(g.tms)$bet.1.0 <- as.data.frame(betweenness_w(tn.tms, alpha=1.0))$betweenness
# Opsahl et al's WEIGHTED CLOSENESS
# alpha = 1.0
V(g.tms)$clo.1.0 <- as.data.frame(closeness_w(tn.tms, alpha=1.0))$n.closeness
# Add blog post lengths as vertex attribute
length <- read.csv("2_doc_lengths.csv")
V(g.tms)$length <- length$length # Prepare simulated graphs for ggplot:
## disconnected ring
l0 <- layout_in_circle(sim.net0)
sim.net0 <- ggnetwork(sim.net0, layout=l0, weight="weight")
## connected ring
l1 <- layout_in_circle(sim.net1)
sim.net1 <- ggnetwork(sim.net1, layout=l1, weight="weight")
## bifurcated clique
l2 <- layout_with_fr(sim.net2)
sim.net2 <- ggnetwork(sim.net2, layout=l2, weight="weight")
## core-periphery
l3 <- layout_with_fr(sim.net3)
sim.net3 <- ggnetwork(sim.net3, layout=l3, weight="weight")
# GRAPHS --------------------------------------------------------------
# Disconnected Ring Graph
# text SPANNING with alpha = 1.0
set.seed(123)
net0.1.0 <- ggplot(sim.net0, aes(x = x, y = y, xend = xend, yend = yend)) +
geom_edges(data=sim.net0[which(sim.net0$weight>=.8),], alpha=.5, color="black") +
geom_edges(data=sim.net0[which(sim.net0$weight<=.5),], alpha=.2, color="gray50") +
geom_nodes(data=sim.net0, aes(x, y, color=span.1.0), size=3) +
theme_blank() +
theme(legend.position="left", plot.title=element_text(face="bold", hjust=.5),
legend.title=element_text(face="bold"),
axis.text = element_blank(),
axis.title = element_blank()) +
labs(title=expression(bold(paste("Disconnected, Spanning ", alpha, " = 1.0")))) +
scale_color_gradient(name="",
limits=c(min(sim.net0$span.1.0), max(sim.net0$span.1.0)),
breaks=c(min(sim.net0$span.1.0), max(sim.net0$span.1.0)),
labels=c("Low","High"),
low = "#fdbf11", high = "#1696d2") +
geom_nodelabel_repel(aes(label = ifelse(sim.net0$span.1.0>=quantile(span.1.0, c(.0)),
as.character(round(span.1.0, digits=3)),'')),
box.padding = unit(1, "lines"))
# Connected Ring Graph
# text SPANNING with alpha = 1.0
set.seed(123)
net1.1.0 <- ggplot(sim.net1, aes(x = x, y = y, xend = xend, yend = yend)) +
geom_edges(data=sim.net1[which(sim.net1$weight>=.8),], alpha=.5, color="black") +
geom_edges(data=sim.net1[which(sim.net1$weight<=.5),], alpha=.2, color="gray50") +
geom_nodes(data=sim.net1, aes(x, y, color=span.1.0), size=3) +
theme_blank() +
theme(legend.position="left", plot.title=element_text(face="bold", hjust=.5),
legend.title=element_text(face="bold"),
axis.text = element_blank(),
axis.title = element_blank()) +
labs(title=expression(bold(paste("Connected, Spanning ", alpha, " = 1.0")))) +
scale_color_gradient(name="",
limits=c(min(sim.net1$span.1.0), max(sim.net1$span.1.0)),
breaks=c(min(sim.net1$span.1.0), max(sim.net1$span.1.0)),
labels=c("Low","High"),
low = "#fdbf11", high = "#1696d2") +
geom_nodelabel_repel(aes(label = ifelse(sim.net1$span.1.0>=quantile(span.1.0, c(.0)),
as.character(round(span.1.0, digits=3)),'')),
box.padding = unit(1, "lines"))
# -----------------------
# Disconnected Ring Graph
# weighted DEGREE with alpha = 1.0
set.seed(123)
net0.1.0d <- ggplot(sim.net0, aes(x = x, y = y, xend = xend, yend = yend)) +
geom_edges(data=sim.net0[which(sim.net0$weight>=.8),], alpha=.5, color="black") +
geom_edges(data=sim.net0[which(sim.net0$weight<=.5),], alpha=.2, color="gray50") +
geom_nodes(data=sim.net0, aes(x, y, color=deg.1.0), size=3) +
theme_blank() +
theme(legend.position="left", plot.title=element_text(face="bold", hjust=.5),
legend.title=element_text(face="bold"),
axis.text = element_blank(),
axis.title = element_blank()) +
labs(title=expression(bold(paste("Disconnected, Degree ", alpha, " = 1.0")))) +
scale_color_gradient(name="",
limits=c(min(sim.net0$deg.1.0), max(sim.net0$deg.1.0)),
breaks=c(min(sim.net0$deg.1.0), max(sim.net0$deg.1.0)),
labels=c("Low","High"),
low = "#fdbf11", high = "#1696d2") +
geom_nodelabel_repel(aes(label = ifelse(sim.net0$deg.1.0>=quantile(deg.1.0, c(.01)),
as.character(round(deg.1.0, digits=3)),'')),
box.padding = unit(1, "lines"))
# Connected Ring Graph
# weighted DEGREE with alpha = 1.0
set.seed(123)
net1.1.0d <- ggplot(sim.net1, aes(x = x, y = y, xend = xend, yend = yend)) +
geom_edges(data=sim.net1[which(sim.net1$weight>=.8),], alpha=.5, color="black") +
geom_edges(data=sim.net1[which(sim.net1$weight<=.5),], alpha=.2, color="gray50") +
geom_nodes(data=sim.net1, aes(x, y, color=deg.1.0), size=3) +
theme_blank() +
theme(legend.position="left", plot.title=element_text(face="bold", hjust=.5),
legend.title=element_text(face="bold"),
axis.text = element_blank(),
axis.title = element_blank()) +
labs(title=expression(bold(paste("Connected, Degree ", alpha, " = 1.0")))) +
scale_color_gradient(name="",
limits=c(min(sim.net1$deg.1.0), max(sim.net1$deg.1.0)),
breaks=c(min(sim.net1$deg.1.0), max(sim.net1$deg.1.0)),
labels=c("Low","High"),
low = "#fdbf11", high = "#1696d2") +
geom_nodelabel_repel(aes(label = ifelse(sim.net1$deg.1.0>=quantile(deg.1.0, c(.01)),
as.character(round(deg.1.0, digits=3)),'')),
box.padding = unit(1, "lines"))
## Arrange Ring Graphs for comparison
pdf("Figure_2_Ring_Graphs.pdf")
ggarrange(net0.1.0, net1.1.0, net0.1.0d, net1.1.0d,
ncol=2, nrow=2, common.legend=T, legend="left")
dev.off()
## --------------------------------------------------------------
# Bifurcated Clique Graph (Spanning)
# alpha = 1.0
set.seed(123)
net2.1.0 <- ggplot(sim.net2, aes(x = x, y = y, xend = xend, yend = yend)) +
geom_edges(data=sim.net2[which(sim.net2$weight==.8),], alpha=.5, color="black") +
geom_edges(data=sim.net2[which(sim.net2$weight==.1),], alpha=.1, color="gray50") +
geom_nodes(data=sim.net2, aes(x, y, color=span.1.0), size=3) +
theme_blank() +
theme(legend.position="left", plot.title=element_text(face="bold", hjust=.5),
legend.title=element_text(face="bold"),
axis.text = element_blank(),
axis.title = element_blank()) +
labs(title=expression(bold(paste("Bifurcated Clique, Spanning ",alpha, " = 1.0")))) +
scale_color_gradient(name="",
limits=c(min(sim.net2$span.1.0), max(sim.net2$span.1.0)),
breaks=c(min(sim.net2$span.1.0), max(sim.net2$span.1.0)),
labels=c("Low","High"),
low = "#fdbf11", high = "#1696d2") +
geom_nodelabel_repel(aes(label = ifelse(sim.net2$span.1.0>=quantile(span.1.0, c(.90)),
as.character(round(span.1.0, digits=3)),'')),
box.padding = unit(1, "lines"))
pdf("Figure_3_Clique_Graph.pdf", width=8, height=6)
net2.1.0
dev.off()
## --------------------------------------------------------------
# Core-Periphery Graph (Spanning)
# alpha = 1.0
set.seed(123)
net3.1.0 <- ggplot(sim.net3, aes(x = x, y = y, xend = xend, yend = yend)) +
geom_edges(data=sim.net3[which(sim.net3$weight==.8),], alpha=.5, color="black") +
geom_edges(data=sim.net3[which(sim.net3$weight==.1),], alpha=.1, color="gray50") +
geom_nodes(data=sim.net3, aes(x, y, color=span.1.0), size=3) +
theme_blank() +
theme(legend.position="left", plot.title=element_text(face="bold", hjust=.5),
legend.title=element_text(face="bold"),
axis.text = element_blank(),
axis.title = element_blank()) +
labs(title=expression(bold(paste("Core-Periphery, ", alpha, " = 1.0")))) +
scale_color_gradient(name="",
limits=c(min(sim.net3$span.1.0), max(sim.net3$span.1.0)),
breaks=c(min(sim.net3$span.1.0), max(sim.net3$span.1.0)),
labels=c("Low","High"),
low = "#fdbf11", high = "#1696d2") +
geom_nodelabel_repel(aes(label = ifelse(sim.net3$span.1.0>=quantile(span.1.0, c(.80)),
as.character(round(span.1.0, digits=3)),'')),
box.padding = unit(1, "lines"))
pdf("Figure_4_Core_Graph.pdf", width=8, height=6)
net3.1.0
dev.off() ## Prepare Topic Model Graph Layout
# Removing edges for visualization
p.tms <- igraph::delete.edges(g.tms, E(g.tms)[weight<.6])
#
l5 <- layout_with_fr(p.tms, niter=2000)
p.tms <- ggnetwork(p.tms, layout=l5, weight="weight")
# Graph colored by TEXTUAL SPANNING Score, alpha = 1.0
set.seed(786)
g.span.1.0 <- ggplot(p.tms, aes(x = x, y = y, xend = xend, yend = yend)) +
geom_edges(alpha=.4, color="gray75") +
geom_nodes(aes(x, y, size=length, color=span.1.0)) +
theme_blank() +
theme(legend.position="left", plot.title=element_text(face="bold", hjust=.5),
legend.title=element_text(face="bold"),
axis.text = element_blank(),
axis.title = element_blank()) +
labs(title=expression(bold(paste("Textual Spanning, ", alpha, " = 1.0")))) +
scale_color_gradient(name="", limits=c(min(p.tms$span.1.0),
max(p.tms$span.1.0)),
breaks=c(min(p.tms$span.1.0), max(p.tms$span.1.0)),
labels=c("Low","High"),
low = "#fdbf11", high = "#1696d2") +
guides(size = guide_legend(reverse=T, title="Length")) +
geom_nodelabel_repel(aes(label = ifelse(p.tms$span.1.0>=quantile(span.1.0, c(.95)),
as.character(round(span.1.0, digits=3)),'')),
box.padding = unit(1, "lines")) +
geom_nodelabel_repel(aes(label = ifelse(p.tms$span.1.0<=quantile(span.1.0, c(.01)),
as.character(round(span.1.0, digits=3)),'')),
box.padding = unit(1, "lines"))
pdf("Figure_6_TMS_Span.pdf", width=11, height=8.5)
g.span.1.0
dev.off()
# Same as above, but without labels
set.seed(786)
g.span.1.0b <- ggplot(p.tms, aes(x = x, y = y, xend = xend, yend = yend)) +
geom_edges(alpha=.4, color="gray75") +
geom_nodes(aes(x, y, size=length, color=span.1.0)) +
theme_blank() +
theme(legend.position="left", plot.title=element_text(face="bold", hjust=.5),
legend.title=element_text(face="bold"),
axis.text = element_blank(),
axis.title = element_blank()) +
labs(title=expression(bold(paste("Textual Spanning, ", alpha, " = 1.0")))) +
scale_color_gradient(name="", limits=c(min(p.tms$span.1.0),
max(p.tms$span.1.0)),
breaks=c(min(p.tms$span.1.0), max(p.tms$span.1.0)),
labels=c("Low","High"),
low = "#fdbf11", high = "#1696d2") +
guides(size = guide_legend(reverse=T, title="Length"))
## --------------------------------------------------------------
# Opsahl et al's WEIGHTED DEGREE, alpha = 1.0
set.seed(786)
g.deg.1.0 <- ggplot(p.tms, aes(x = x, y = y, xend = xend, yend = yend)) +
geom_edges(alpha=.4, color="gray75") +
geom_nodes(aes(x, y, size=length, color=deg.1.0)) +
theme_blank() +
theme(legend.position="left", plot.title=element_text(face="bold", hjust=.5),
legend.title=element_text(face="bold"),
axis.text = element_blank(),
axis.title = element_blank()) +
labs(title=expression(bold(paste("Weighted Degree, ", alpha, " = 1.0")))) +
scale_color_gradient(name=" ", limits=c(min(p.tms$deg.1.0),
max(p.tms$deg.1.0)),
breaks=c(min(p.tms$deg.1.0), max(p.tms$deg.1.0)),
labels=c("Low","High"),
low = "#fdbf11", high = "#1696d2") +
guides(size = guide_legend(reverse=T, title="Length"))
## --------------------------------------------------------------
# Opsahl et al's WEIGHTED BETWEENNESS
set.seed(786)
g.bet.1.0 <- ggplot(p.tms, aes(x = x, y = y, xend = xend, yend = yend)) +
geom_edges(alpha=.4, color="gray75") +
geom_nodes(aes(x, y, size=length, color=bet.1.0)) +
theme_blank() +
theme(legend.position="left", plot.title=element_text(face="bold", hjust=.5),
legend.title=element_text(face="bold"),
axis.text = element_blank(),
axis.title = element_blank()) +
labs(title=expression(bold(paste("Weighted Betweeness, ", alpha, " = 1.0")))) +
scale_color_gradient(name=" ", limits=c(min(p.tms$bet.1.0),
max(p.tms$bet.1.0)),
breaks=c(min(p.tms$bet.1.0), max(p.tms$bet.1.0)),
labels=c("Low","High"),
low = "#fdbf11", high = "#1696d2") +
guides(size = guide_legend(reverse=T, title="Length"))
## --------------------------------------------------------------
# Opsahl et al's WEIGHTED CLOSENESS
set.seed(786)
g.clo.1.0 <- ggplot(p.tms, aes(x = x, y = y, xend = xend, yend = yend)) +
geom_edges(alpha=.4, color="gray75") +
geom_nodes(aes(x, y, size=length, color=clo.1.0)) +
theme_blank() +
theme(legend.position="left", plot.title=element_text(face="bold", hjust=.5),
legend.title=element_text(face="bold"),
axis.text = element_blank(),
axis.title = element_blank()) +
labs(title=expression(bold(paste("Weighted Closeness, ", alpha, " = 1.0")))) +
scale_color_gradient(name=" ", limits=c(min(p.tms$clo.1.0),
max(p.tms$clo.1.0)),
breaks=c(min(p.tms$clo.1.0), max(p.tms$clo.1.0)),
labels=c("Low","High"),
low = "#fdbf11", high = "#1696d2") +
guides(size = guide_legend(reverse=T, title="Length"))
## Arrange Graphs w/ Opsahl et al's measures side by side
pdf("Figure_7_TMS_Opsahl.pdf", width=11, height=10)
ggarrange(g.span.1.0b, g.deg.1.0, g.bet.1.0, g.clo.1.0,
ncol=2, nrow=2, common.legend=T, legend="bottom")
dev.off() cor <- data.frame()[1:100, ]
head(cor)
cor$tms.span.1.0 <- V(g.tms)$span.1.0
cor$tms.deg.1.0 <- V(g.tms)$deg.1.0
cor$tms.bet.1.0 <- V(g.tms)$bet.1.0
cor$tms.clo.1.0 <- V(g.tms)$clo.1.0
cor$length <- length$length
colnames(cor) <- c("Spanning","Degree", "Betweenness","Closeness", "Length")
## Calculate correlation using Performance Analytics package
pdf("Figure_8_Correlations.pdf", width=7, height=5)
chart.Correlation(cor, histogram=T, pch=19, method="pearson")
dev.off() ## Create data frame
names <- V(g.tms)$name
spanning <- V(g.tms)$span.1.0
degree <- V(g.tms)$deg.1.0
between <- V(g.tms)$bet.1.0
close <- V(g.tms)$clo.1.0
span.mat <- cbind(names, spanning, degree, between, close)
span.mat <- as.data.frame(span.mat)
span.mat <- span.mat[order(-spanning),]
# head(span.mat, n=5)
## Box plots
span.mat$degree <- as.numeric(as.character(span.mat$degree))
span.mat$between <- as.numeric(as.character(span.mat$between))
span.mat$close <- as.numeric(as.character(span.mat$close))
span.mat$sddegree <- ((span.mat$degree - mean(span.mat$degree))/sd(span.mat$degree))
span.mat$sdbetween <- ((span.mat$between - mean(span.mat$between))/sd(span.mat$between))
span.mat$sdclose <- ((span.mat$close - mean(span.mat$close))/sd(span.mat$close))
span.mat2 <- melt(span.mat, id.vars="names")
span.mat2$color[which(span.mat2$variable=="spanning")] <- "#1696d2"
span.mat2$color[which(span.mat2$variable=="sddegree")] <- "#ec008b"
span.mat2$color[which(span.mat2$variable=="sdbetween")] <- "#fdbf11"
span.mat2$color[which(span.mat2$variable=="sdclose")] <- "#5c5859"
span.mat2$color[which(span.mat2$names=="6527" | span.mat2$names=="8218" |
span.mat2$names=="8258" | span.mat2$names=="5286" |
span.mat2$names=="1926")] <- "black"
span.mat2$shape <- NA
span.mat2$shape[which(span.mat2$color=="black")] <- 1
span.mat2$shape[which(span.mat2$color!="black")] <- 0
##
box <- ggplot(span.mat2[which(span.mat2$variable=="spanning" | span.mat2$variable=="sddegree" |
span.mat2$variable=="sdbetween" | span.mat2$variable=="sdclose"),],
aes(x=as.factor(variable), y=as.numeric(value), fill=as.factor(variable))) +
geom_boxplot(aes(fill=variable), alpha=.5, outlier.shape=NA) +
geom_jitter(aes(color=as.factor(color), shape=as.factor(shape),
size=shape)) +
ylab("Centrality (Standardized)") + xlab("") +
theme_bw() +
theme(axis.text.x=element_blank(), axis.ticks.x=element_blank(),
legend.title=element_text(face="bold")) +
scale_fill_manual(name="Centrality\nMeasures",
breaks=c("spanning","sddegree","sdbetween","sdclose"),
labels=c("Spanning","W Degree","W Between","W Closeness"),
values=c("#1696d2","#ec008b","#fdbf11","#55b748")) +
scale_color_manual(values=c("#1696d2","#55b748","#ec008b","#fdbf11","black")) +
scale_size_continuous(range = c(1,2)) +
guides(color=F) +
guides(shape=F) +
guides(size=F)
##
pdf("Figure_9_box_plot.pdf", width=8, height=6)
box
dev.off() cor.tms <- cor(tms)
cor.tms <- ifelse(cor.tms<0, 0, cor.tms)
new.names <- paste("T", seq(1:20), sep="")
##
qgraph(cor.tms, graph="cor", layout="spring", posCol="black",
threshold=.1, labels=new.names,
color=ifelse(rownames(cor.tms)=="V18" |
rownames(cor.tms)=="V14", "#fdbf11","#d2d2d2"),
vsize=5,
filename="Figure_10_Correlation_Network",
filetype="pdf")