#install.packages("igraph")
require(igraph)
g <- make_graph("Zachary")
plot(g)
gmat <- as_adjacency_matrix(g, type = "both", sparse = FALSE)
gmat## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13] [,14] [,15] [,16] [,17]
## [1,] 0 1 1 1 1 1 1 1 1 0 1 1 1 1 0 0 0
## [2,] 1 0 1 1 0 0 0 1 0 0 0 0 0 1 0 0 0
## [3,] 1 1 0 1 0 0 0 1 1 1 0 0 0 1 0 0 0
## [4,] 1 1 1 0 0 0 0 1 0 0 0 0 1 1 0 0 0
## [5,] 1 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0
## [6,] 1 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 1
## [7,] 1 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 1
## [8,] 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0
## [9,] 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [10,] 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [11,] 1 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0
## [12,] 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [13,] 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0
## [14,] 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0
## [15,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [16,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [17,] 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0
## [18,] 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [19,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [20,] 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [21,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [22,] 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [23,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [24,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [25,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [26,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [27,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [28,] 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [29,] 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [30,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [31,] 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0
## [32,] 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [33,] 0 0 1 0 0 0 0 0 1 0 0 0 0 0 1 1 0
## [34,] 0 0 0 0 0 0 0 0 1 1 0 0 0 1 1 1 0
## [,18] [,19] [,20] [,21] [,22] [,23] [,24] [,25] [,26] [,27] [,28] [,29] [,30] [,31] [,32]
## [1,] 1 0 1 0 1 0 0 0 0 0 0 0 0 0 1
## [2,] 1 0 1 0 1 0 0 0 0 0 0 0 0 1 0
## [3,] 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0
## [4,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [5,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [6,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [7,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [8,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [9,] 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0
## [10,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [11,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [12,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [13,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [14,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [15,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [16,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [17,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [18,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [19,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [20,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [21,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [22,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [23,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [24,] 0 0 0 0 0 0 0 0 1 0 1 0 1 0 0
## [25,] 0 0 0 0 0 0 0 0 1 0 1 0 0 0 1
## [26,] 0 0 0 0 0 0 1 1 0 0 0 0 0 0 1
## [27,] 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0
## [28,] 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0
## [29,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
## [30,] 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0
## [31,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [32,] 0 0 0 0 0 0 0 1 1 0 0 1 0 0 0
## [33,] 0 1 0 1 0 1 1 0 0 0 0 0 1 1 1
## [34,] 0 1 1 1 0 1 1 0 0 1 1 1 1 1 1
## [,33] [,34]
## [1,] 0 0
## [2,] 0 0
## [3,] 1 0
## [4,] 0 0
## [5,] 0 0
## [6,] 0 0
## [7,] 0 0
## [8,] 0 0
## [9,] 1 1
## [10,] 0 1
## [11,] 0 0
## [12,] 0 0
## [13,] 0 0
## [14,] 0 1
## [15,] 1 1
## [16,] 1 1
## [17,] 0 0
## [18,] 0 0
## [19,] 1 1
## [20,] 0 1
## [21,] 1 1
## [22,] 0 0
## [23,] 1 1
## [24,] 1 1
## [25,] 0 0
## [26,] 0 0
## [27,] 0 1
## [28,] 0 1
## [29,] 0 1
## [30,] 1 1
## [31,] 1 1
## [32,] 1 1
## [33,] 0 1
## [34,] 1 0vcount(g)## [1] 34ecount(g)## [1] 78degree(g)## [1] 16 9 10 6 3 4 4 4 5 2 3 1 2 5 2 2 2 2 2 3 2 2 2 5 3 3 2 4 3 4 4 6
## [33] 12 17hist(table(degree(g)), xlab='indegree', main= 'Histogram of indegree')
# be aware that directed graphs are considered as undirected. but g is undirected.
transitivity(g, type = c("localundirected"), isolates = c("NaN", "zero"))## [1] 0.1500000 0.3333333 0.2444444 0.6666667 0.6666667 0.5000000 0.5000000 1.0000000 0.5000000
## [10] 0.0000000 0.6666667 NaN 1.0000000 0.6000000 1.0000000 1.0000000 1.0000000 1.0000000
## [19] 1.0000000 0.3333333 1.0000000 1.0000000 1.0000000 0.4000000 0.3333333 0.3333333 1.0000000
## [28] 0.1666667 0.3333333 0.6666667 0.5000000 0.2000000 0.1969697 0.1102941dyad.census(g)## $mut
## [1] 78
##
## $asym
## [1] 0
##
## $null
## [1] 483#install.packages("sna")
igraph::triad.census(g)## Warning in igraph::triad.census(g): At core/misc/motifs.c:1165 : Triad census called on an
## undirected graph.## [1] 3971 0 1575 0 0 0 0 0 0 0 393 0 0 0 0 45# I will use sna because it shows the names of the triads as well.
sna::triad.census(gmat)## 003 012 102 021D 021U 021C 111D 111U 030T 030C 201 120D 120U 120C 210 300
## [1,] 3971 0 1575 0 0 0 0 0 0 0 393 0 0 0 0 45# changing V
V(g)$size = degree(g)*3
plot(g, mode = "undirected")
g## IGRAPH 27d6804 U--- 34 78 -- Zachary
## + attr: name (g/c), size (v/n)
## + edges from 27d6804:
## [1] 1-- 2 1-- 3 1-- 4 1-- 5 1-- 6 1-- 7 1-- 8 1-- 9 1--11 1--12 1--13 1--14 1--18
## [14] 1--20 1--22 1--32 2-- 3 2-- 4 2-- 8 2--14 2--18 2--20 2--22 2--31 3-- 4 3-- 8
## [27] 3--28 3--29 3--33 3--10 3-- 9 3--14 4-- 8 4--13 4--14 5-- 7 5--11 6-- 7 6--11
## [40] 6--17 7--17 9--31 9--33 9--34 10--34 14--34 15--33 15--34 16--33 16--34 19--33 19--34
## [53] 20--34 21--33 21--34 23--33 23--34 24--26 24--28 24--33 24--34 24--30 25--26 25--28 25--32
## [66] 26--32 27--30 27--34 28--34 29--32 29--34 30--33 30--34 31--33 31--34 32--33 32--34 33--34summary(g)## IGRAPH 27d6804 U--- 34 78 -- Zachary
## + attr: name (g/c), size (v/n)head(g)## 6 x 34 sparse Matrix of class "dgCMatrix"
##
## [1,] . 1 1 1 1 1 1 1 1 . 1 1 1 1 . . . 1 . 1 . 1 . . . . . . . . . 1 . .
## [2,] 1 . 1 1 . . . 1 . . . . . 1 . . . 1 . 1 . 1 . . . . . . . . 1 . . .
## [3,] 1 1 . 1 . . . 1 1 1 . . . 1 . . . . . . . . . . . . . 1 1 . . . 1 .
## [4,] 1 1 1 . . . . 1 . . . . 1 1 . . . . . . . . . . . . . . . . . . . .
## [5,] 1 . . . . . 1 . . . 1 . . . . . . . . . . . . . . . . . . . . . . .
## [6,] 1 . . . . . 1 . . . 1 . . . . . 1 . . . . . . . . . . . . . . . . .# start with clean workspace
rm(list = ls())load("./data/soc_df.RData")
load("./data/soc_collabs1.RData")
load("./data/soc_collabs2.RData")structure ("soc_df.RData")## [1] "soc_df.RData"summary ("soc_df.RData")## Length Class Mode
## 1 character character# co-authors networks
# get unique collaborators of soc staff first, zodat je niet iemand dubbel hebt. Alle mensen verzamelen, dus staffleden sociologie en alle co-auteurs.
soc_collabs_unique <- unique(soc_collabs[, 4]) # so 230 unique collaborators for RU staff?
summary(soc_collabs_unique)## Length Class Mode
## 230 character charactersoc_collabs_unique <- c(soc_collabs_unique, soc_df$gs_id) # add the soc staff themselves.New people who have not worked together with others.
soc_collabs_unique <- data.frame(soc_collabs_unique)
soc_collabs_unique$v1 <- 1 # convenient to select on after the merge
soc_collabs_unique <- unique(soc_collabs_unique)
# so this is a very important step, we join the unique soc collaborators to the collaborators of
# collaborators
require(tidyverse)
onefivedegree <- left_join(collabs_1deep, soc_collabs_unique, by = c(coauth_id = "soc_collabs_unique"))
# Then, we drop those that are not among sociology collaborators and who don't lsit coauthors
# regardless
onefivedegree <- onefivedegree[!is.na(onefivedegree$v1), ]
onefivedegree <- onefivedegree[!is.na(onefivedegree$coauth), ]
# we pick those columns and have an edgelist of soc collaborators and whether they collaborate with
# those same collaborators the 1.5 degree network
onefivedegree <- onefivedegree[, c("name", "coauth")]
names(onefivedegree) <- c("from", "to")
# we get soc collaborators and add those to the data above and end up with a nice edgelist!
socc <- soc_collabs[!is.na(soc_collabs$coauth), ]
socc <- socc[, c("name", "coauth")]
names(socc) <- c("from", "to")
onefivedegree <- rbind(socc, onefivedegree)
save(onefivedegree, file = "Data/soc_onefivedegree.RData")#plotting it.
onefivedegree <- as.matrix(onefivedegree) # matrix because igraph wants that
library(igraph) # Notice that we call igraph here, in anticipation of the viz tutorial.
net1 <- graph_from_edgelist(onefivedegree, directed = TRUE) # Notice the igraph function here!
plot(net1) # pretty ugly!
#making it prettier
plot(net1 ,
vertex.color = "gold", # nice color for the vertices
vertex.size = 4, # we'll vertices a bit smaller
vertex.frame.color = "gray", # we'll put a gray frame around vertices
vertex.label.color = "black", # not that ugly blue color for the labels (names)
vertex.label.family = "Helvetica", # not a fan of times new roman in figures
vertex.label.cex = 0.4, # make the label a bit smaller too
vertex.label.dist = 0.5, # we'll pull the labels a bit away from the vertices
edge.curved = 0.2, # curved edges is always a nice touch
edge.arrow.size = 0.1) # make arrow size (direction of edge) smaller## Warning in text.default(x, y, labels = labels, col = label.color, family = label.family, : font
## family not found in Windows font database
#single out staff members.
#gather the vertices in the network object as data frame
in_network <- data.frame(as_ids(V(net1)))
names(in_network)[1] <- "name"
#soc_df$total_cites <- soc_df$total_cites.x
# collect the Radboud sociology nodes from the soc_df data frame
ru_nodes <- soc_df[, c("name", "total_cites")]
# join with the in_network dataset and put some different color label on those nodes who were in the soc_df and those who were not.
in_network <- left_join(in_network, ru_nodes, by = c("name" = "name"))
in_network$vcol <- ifelse(is.na(in_network$total_cites), "#E69F00", "#56B4E9")
plot(net1,
vertex.color = in_network$vcol, #THIS WAS WHAT WE DID THE LAST CODEBLOCK FOR!
vertex.size = 4, # we'll make them a bit smaller
vertex.frame.color = "gray", # we'll put a frame around it
vertex.label.color = "black", # not that ugly blue color for the names
vertex.label.family = "Helvetica", # not a fan of times in figures
vertex.label.cex = 0.4, # a bit smaller too
vertex.label.dist = 0.5, # we'll pull the labels a bit away from the vertices
edge.curved = 0.2, # curved edges is always a nice tough
edge.arrow.size = 0.1) # arrow size smaller## Warning in text.default(x, y, labels = labels, col = label.color, family = label.family, : font
## family not found in Windows font database
#show only the labels of RU sociology staff.
plot(net1,
vertex.color = in_network$vcol,
#NOTICE THESE CONDITIONAL STATEMENTS BELOW
vertex.label = ifelse(!is.na(in_network$total_cites), in_network$name, NA),
vertex.size = 4, # we'll make them a bit smaller
vertex.frame.color = "gray", # we'll put a frame around it
vertex.label.color = "black", # not that ugly blue color for the names
vertex.label.family = "Helvetica", # not a fan of times in figures
vertex.label.cex = 0.65, # a bit smaller too
vertex.label.dist = 0.5, # we'll pull the labels a bit away from the vertices
edge.curved = 0.2, # curved edges is always a nice tough
edge.arrow.size = 0.1) # arrow size smaller## Warning in text.default(x, y, labels = labels, col = label.color, family = label.family, : font
## family not found in Windows font database
#Vertex size adjusted for citation
plot(net1,
vertex.color = in_network$vcol,
vertex.label = ifelse(!is.na(in_network$total_cites), in_network$name, NA),
# SAME HERE, TRY TO SMOOTH THE TOTAL_CITES A BIT WITH LOGSCALE
vertex.size = ifelse(!is.na(in_network$total_cites), log(in_network$total_cites), 2),
vertex.frame.color = "gray", # we'll put a frame around it
vertex.label.color = "black", # not that ugly blue color for the names
vertex.label.family = "Helvetica", # not a fan of times in figures
vertex.label.cex = 0.65, # a bit smaller too
vertex.label.dist = 0.5, # we'll pull the labels a bit away from the vertices
edge.curved = 0.2, # curved edges is always a nice tough
edge.arrow.size = 0.1) # arrow size smaller## Warning in text.default(x, y, labels = labels, col = label.color, family = label.family, : font
## family not found in Windows font database
#New lay-out
#Vertex size adjusted for citation
plot(net1,
vertex.color = in_network$vcol,
vertex.shapes = c("square"),
vertex.label = ifelse(!is.na(in_network$total_cites), in_network$name, NA),
# SAME HERE, TRY TO SMOOTH THE TOTAL_CITES A BIT WITH LOGSCALE
vertex.size = ifelse(!is.na(in_network$total_cites), log(in_network$total_cites), 6),
vertex.frame.color = "black", # we'll put a frame around it
vertex.label.color = "black", # not that ugly blue color for the names
vertex.arrow.color = "black",
vertex.label.family = "Helvetica", # not a fan of times in figures
vertex.label.cex = 0.6, # a bit smaller too
vertex.label.dist = 0.8, # we'll pull the labels a bit away from the vertices
edge.curved = 0.2, # curved edges is always a nice tough
edge.lty=c("dashed"),
edge.arrow.size = 0.1) # arrow size smaller## Warning in text.default(x, y, labels = labels, col = label.color, family = label.family, : font
## family not found in Windows font database
#Changing the lay-out further
#Vertex size adjusted for citation
par(bg="black")
plot(net1,
vertex.color = in_network$vcol,
vertex.label = ifelse(!is.na(in_network$total_cites), in_network$name, NA),
# SAME HERE, TRY TO SMOOTH THE TOTAL_CITES A BIT WITH LOGSCALE
vertex.size = ifelse(!is.na(in_network$total_cites), log(in_network$total_cites), 2),
vertex.frame.color = "yellow", # we'll put a frame around it
vertex.label.color = "white", # not that ugly blue color for the names
vertex.label.family = "Helvetica", # not a fan of times in figures
vertex.shape="square",
vertex.label.cex = 0.65, # a bit smaller too
vertex.label.dist = 0.5, # we'll pull the labels a bit away from the vertices
edge.curved = 0.2, # curved edges is always a nice tough
edge.arrow.size = 0.1) # arrow size smaller## Warning in text.default(x, y, labels = labels, col = label.color, family = label.family, : font
## family not found in Windows font database
```{r}
#install.packages("igraph")

require(igraph)
g <- make_graph("Zachary")
plot(g)
```

```{r}
gmat <- as_adjacency_matrix(g, type = "both", sparse = FALSE)
gmat
```

```{r}
vcount(g)
ecount(g)
```

```{r}
degree(g)
hist(table(degree(g)), xlab='indegree', main= 'Histogram of indegree')
```

```{r}
# be aware that directed graphs are considered as undirected. but g is undirected.
transitivity(g, type = c("localundirected"), isolates = c("NaN", "zero"))
```

```{r}
dyad.census(g)
```

```{r}
#install.packages("sna")
igraph::triad.census(g)
# I will use sna because it shows the names of the triads as well.
sna::triad.census(gmat)
```

```{r}
# changing V
V(g)$size = degree(g)*3
plot(g, mode = "undirected")
```

```{r}
g
summary(g)
head(g)
```

```{r}
# start with clean workspace
rm(list = ls())
```


```{r}
load("./data/soc_df.RData")
load("./data/soc_collabs1.RData")
load("./data/soc_collabs2.RData")
```

```{r}
structure ("soc_df.RData")
summary ("soc_df.RData")
```

```{r}
# co-authors networks


# get unique collaborators of soc staff first, zodat je niet iemand dubbel hebt. Alle mensen verzamelen, dus staffleden sociologie en alle co-auteurs.
soc_collabs_unique <- unique(soc_collabs[, 4])  # so 230 unique collaborators for RU staff?
summary(soc_collabs_unique)
soc_collabs_unique <- c(soc_collabs_unique, soc_df$gs_id)  # add the soc staff themselves.New people who have not worked together with others.
soc_collabs_unique <- data.frame(soc_collabs_unique)
soc_collabs_unique$v1 <- 1  # convenient to select on after the merge
soc_collabs_unique <- unique(soc_collabs_unique)

# so this is a very important step, we join the unique soc collaborators to the collaborators of
# collaborators
require(tidyverse)
onefivedegree <- left_join(collabs_1deep, soc_collabs_unique, by = c(coauth_id = "soc_collabs_unique"))

# Then, we drop those that are not among sociology collaborators and who don't lsit coauthors
# regardless
onefivedegree <- onefivedegree[!is.na(onefivedegree$v1), ]
onefivedegree <- onefivedegree[!is.na(onefivedegree$coauth), ]

# we pick those columns and have an edgelist of soc collaborators and whether they collaborate with
# those same collaborators the 1.5 degree network
onefivedegree <- onefivedegree[, c("name", "coauth")]
names(onefivedegree) <- c("from", "to")

# we get soc collaborators and add those to the data above and end up with a nice edgelist!
socc <- soc_collabs[!is.na(soc_collabs$coauth), ]
socc <- socc[, c("name", "coauth")]
names(socc) <- c("from", "to")
onefivedegree <- rbind(socc, onefivedegree)
save(onefivedegree, file = "Data/soc_onefivedegree.RData")
```

```{r}
#plotting it.
onefivedegree <- as.matrix(onefivedegree)  # matrix because igraph wants that
library(igraph)  # Notice that we call igraph here, in anticipation of the viz tutorial.
net1 <- graph_from_edgelist(onefivedegree, directed = TRUE)  # Notice the igraph function here!

plot(net1)  # pretty ugly!
```

```{r}
#making it prettier
plot(net1 ,
     vertex.color = "gold", # nice color for the vertices
     vertex.size = 4,  # we'll vertices a bit smaller
     vertex.frame.color = "gray",  # we'll put a gray frame around vertices
     vertex.label.color = "black",  # not that ugly blue color for the labels (names)
     vertex.label.family = "Helvetica", # not a fan of times new roman in figures
     vertex.label.cex = 0.4,  # make the label a bit smaller too
     vertex.label.dist = 0.5,  # we'll pull the labels a bit away from the vertices
     edge.curved = 0.2, # curved edges is always a nice touch
     edge.arrow.size = 0.1) # make arrow size (direction of edge) smaller
```

```{r}
#single out staff members. 

#gather the vertices in the network object as data frame
in_network <- data.frame(as_ids(V(net1)))
names(in_network)[1] <- "name"

#soc_df$total_cites <- soc_df$total_cites.x
# collect the Radboud sociology nodes from the soc_df data frame
ru_nodes <- soc_df[, c("name", "total_cites")]
# join with the in_network dataset and put some different color label on those nodes who were in the soc_df and those who were not.
in_network <- left_join(in_network, ru_nodes, by = c("name" = "name"))
in_network$vcol <- ifelse(is.na(in_network$total_cites), "#E69F00", "#56B4E9")

plot(net1,
     vertex.color = in_network$vcol, #THIS WAS WHAT WE DID THE LAST CODEBLOCK FOR!
     vertex.size = 4,  # we'll make them a bit smaller
     vertex.frame.color = "gray",  # we'll put a frame around it
     vertex.label.color = "black",  # not that ugly blue color for the names
     vertex.label.family = "Helvetica", # not a fan of times in figures
     vertex.label.cex = 0.4,  # a bit smaller too
     vertex.label.dist = 0.5,  # we'll pull the labels a bit away from the vertices
     edge.curved = 0.2,  # curved edges is always a nice tough
     edge.arrow.size = 0.1) # arrow size smaller


```

```{r}
#show only the labels of RU sociology staff.
plot(net1,
     vertex.color = in_network$vcol, 
     #NOTICE THESE CONDITIONAL STATEMENTS BELOW
     vertex.label = ifelse(!is.na(in_network$total_cites), in_network$name, NA),
     vertex.size = 4,  # we'll make them a bit smaller
     vertex.frame.color = "gray",  # we'll put a frame around it
     vertex.label.color = "black",  # not that ugly blue color for the names
     vertex.label.family = "Helvetica", # not a fan of times in figures
     vertex.label.cex = 0.65,  # a bit smaller too
     vertex.label.dist = 0.5,  # we'll pull the labels a bit away from the vertices
     edge.curved = 0.2, # curved edges is always a nice tough
     edge.arrow.size = 0.1) # arrow size smaller
```

```{r}
#Vertex size adjusted for citation
plot(net1,
     vertex.color = in_network$vcol, 
     vertex.label = ifelse(!is.na(in_network$total_cites), in_network$name, NA),
     # SAME HERE, TRY TO SMOOTH THE TOTAL_CITES A BIT WITH LOGSCALE
     vertex.size = ifelse(!is.na(in_network$total_cites), log(in_network$total_cites), 2),
     vertex.frame.color = "gray",  # we'll put a frame around it
     vertex.label.color = "black",  # not that ugly blue color for the names
     vertex.label.family = "Helvetica", # not a fan of times in figures
     vertex.label.cex = 0.65,  # a bit smaller too
     vertex.label.dist = 0.5,  # we'll pull the labels a bit away from the vertices
     edge.curved = 0.2, # curved edges is always a nice tough
     edge.arrow.size = 0.1) # arrow size smaller
```

```{r}
#New lay-out
#Vertex size adjusted for citation
plot(net1,
     vertex.color = in_network$vcol,
     vertex.shapes = c("square"),
     vertex.label = ifelse(!is.na(in_network$total_cites), in_network$name, NA),
     # SAME HERE, TRY TO SMOOTH THE TOTAL_CITES A BIT WITH LOGSCALE
     vertex.size = ifelse(!is.na(in_network$total_cites), log(in_network$total_cites), 6),
     vertex.frame.color = "black",  # we'll put a frame around it
     vertex.label.color = "black",  # not that ugly blue color for the names
     vertex.arrow.color = "black",
     vertex.label.family = "Helvetica", # not a fan of times in figures
     vertex.label.cex = 0.6,  # a bit smaller too
     vertex.label.dist = 0.8,  # we'll pull the labels a bit away from the vertices
     edge.curved = 0.2, # curved edges is always a nice tough
     edge.lty=c("dashed"),
     edge.arrow.size = 0.1) # arrow size smaller
```

```{r}
#Changing the lay-out further
#Vertex size adjusted for citation
par(bg="black")
plot(net1,
     vertex.color = in_network$vcol, 
     vertex.label = ifelse(!is.na(in_network$total_cites), in_network$name, NA),
     # SAME HERE, TRY TO SMOOTH THE TOTAL_CITES A BIT WITH LOGSCALE
     vertex.size = ifelse(!is.na(in_network$total_cites), log(in_network$total_cites), 2),
     vertex.frame.color = "yellow",  # we'll put a frame around it
     vertex.label.color = "white",  # not that ugly blue color for the names
     vertex.label.family = "Helvetica", # not a fan of times in figures
     vertex.shape="square",
     vertex.label.cex = 0.65,  # a bit smaller too
     vertex.label.dist = 0.5,  # we'll pull the labels a bit away from the vertices
     edge.curved = 0.2, # curved edges is always a nice tough
     edge.arrow.size = 0.1) # arrow size smaller
```

