http://kateto.net/networks-r-igraph

library(igraph)

## 
## Attaching package: 'igraph'

## The following objects are masked from 'package:stats':
## 
##     decompose, spectrum

## The following object is masked from 'package:base':
## 
##     union

g1<- graph(edges = c(1,2,2,3,3,1), n= 3, directed = F)
plot(g1)

class(g1)

## [1] "igraph"

#now with 10 vertices, directed by default
g2<- graph(edges = c(1,2,2,3,3,1) , n= 10)
plot(g2)

g3<-graph(c("John","Jim","Jim","Jill","Jill","John"))
plot(g3)

g4 <- graph(c("John","Jim","Jim","Jack","Jim","Jack","John","John"),
      isolates=c("Jesse","Janis","Jennifer","Justin"))
plot(g4, edge.arrow.size=0.5, vertex.color="gold",vertex.size=15,
     vertex.frame.color="gray",vertex.label.color="black",
     vertex.label.cex=0.8, vertex.label.dist=2, edge.curved=0.2)

#small graphs
plot(graph_from_literal(a -- b , b --c ))# # of dashes doesnt matter

plot(graph_from_literal(a+B,b++c))

plot(graph_from_literal(a+b,b++c))

Edge & Vertex Attributes

# the edges of the object
E(g4) 

## + 4/4 edges from 556e1cb (vertex names):
## [1] John->Jim  Jim ->Jack Jim ->Jack John->John

#the vertices of the object
V(g4)

## + 7/7 vertices, named, from 556e1cb:
## [1] John     Jim      Jack     Jesse    Janis    Jennifer Justin

g4[]  #examine the matrix

## 7 x 7 sparse Matrix of class "dgCMatrix"
##          John Jim Jack Jesse Janis Jennifer Justin
## John        1   1    .     .     .        .      .
## Jim         .   .    2     .     .        .      .
## Jack        .   .    .     .     .        .      .
## Jesse       .   .    .     .     .        .      .
## Janis       .   .    .     .     .        .      .
## Jennifer    .   .    .     .     .        .      .
## Justin      .   .    .     .     .        .      .

g4[1,]

##     John      Jim     Jack    Jesse    Janis Jennifer   Justin 
##        1        1        0        0        0        0        0

V(g4)$name

## [1] "John"     "Jim"      "Jack"     "Jesse"    "Janis"    "Jennifer"
## [7] "Justin"

V(g4)$gender <- c("male","male","male","male","female","female","male")

E(g4)$type<- "email" # Edge attribute, assign "email" to all edges

E(g4)#weight<-10

## + 4/4 edges from 556e1cb (vertex names):
## [1] John->Jim  Jim ->Jack Jim ->Jack John->John

# Edge weight, all edges

plot(g4)

Another way to set attributes

g4<- set_graph_attr(g4,"name","Email Network")

g4<- set_graph_attr(g4, "something" , "A thing")

graph_attr_names(g4)

## [1] "name"      "something"

graph_attr(g4,"name")

## [1] "Email Network"

graph_attr(g4)

## $name
## [1] "Email Network"
## 
## $something
## [1] "A thing"

# delete attribute
g4<- delete_graph_attr(g4, "something")
graph_attr(g4)

## $name
## [1] "Email Network"

plot(g4,edge.arrow.size= 0.5, vertex.label.color= "black", vertex.label.dist= 1.5, vertex.color=c("pink","skyblue")[1+(V(g4)$gender=="male")])

plot(g4,edge.arrow.size= 2, vertex.label.color= "green", vertex.label.dist= 3, vertex.color=c("pink","skyblue")[1+(V(g4)$gender=="male")])

we can simplify graph and remove loops and multi arrows with edge.attr.comb. options: sum,mean,prod,min,max,first/last or “ignore” g4s <- simplify( g4, remove.multiple = T, remove.loops = F,

             edge.attr.comb=c(weight="sum", type="ignore") )

plot(g4s, vertex.label.dist=1.5)

Specific Graphs and Graph Models

eg<- make_empty_graph(40)
plot(eg, vertex.size=10, vertex.label=NA)

Full Graph

fg<-make_full_graph(40)
plot(fg,vertex.size=10, vertex.label= NA)

Simple Star Graph

st<- make_star(40)
plot(st, vertex.size=10, vertex.label=NA)

Tree Graph

tr<- make_tree(40, children = 3, mode= "undirected")
plot(tr, vertex.size=10, vetex.label=NA)

Ring Graph

rn<-make_ring(400)
plot(rn,vertex.size=10, vertex.label=NA)

Erdos-Renyi Random Graph Model

er<- sample_gnm(n=100, m= 40)
plot(er, vertex.size=6 , vertex.label=NA)

Watts-Strogatz small-world model

sw <- sample_smallworld(dim=2, size=10, nei=1, p=0.1)

plot(sw, vertex.size=6, vertex.label=NA, layout=layout_in_circle)

## There are many, many kinds of graphs. See link.

There is a section on importing and using Social Media Files. Data provided

here is the data

http://www.kateto.net/wordpress/wp-content/uploads/2016/01/netscix2016.zip

nodes <- read.csv("Dataset1-Media-Example-NODES.csv", header=T, as.is=T)

links <- read.csv("Dataset1-Media-Example-EDGES.csv", header=T, as.is=T)

Examine the data

head(nodes)

##    id               media media.type type.label audience.size
## 1 s01            NY Times          1  Newspaper            20
## 2 s02     Washington Post          1  Newspaper            25
## 3 s03 Wall Street Journal          1  Newspaper            30
## 4 s04           USA Today          1  Newspaper            32
## 5 s05            LA Times          1  Newspaper            20
## 6 s06       New York Post          1  Newspaper            50

head(links)

##   from  to weight      type
## 1  s01 s02     10 hyperlink
## 2  s01 s02     12 hyperlink
## 3  s01 s03     22 hyperlink
## 4  s01 s04     21 hyperlink
## 5  s04 s11     22   mention
## 6  s05 s15     21   mention

nrow(nodes); length(unique(nodes$id))

## [1] 17

## [1] 17

nrow(links); nrow(unique(links[,c("from", "to")]))

## [1] 52

## [1] 49

Notice that there are more links than unique from-to combinations. That means we have cases in the data where there are multiple links between the same two nodes. We will collapse all links of the same type between the same two nodes by summing their weights, using aggregate() by “from”, “to”, & “type”. We don’t use simplify() here so as not to collapse different link types.

links <- aggregate(links[,3], links[,-3], sum)

links <- links[order(links$from, links$to),]

colnames(links)[4] <- "weight"

rownames(links) <- NULL

Two-mode or bipartite graphs have two different types of actors and links# DATASET 2: matrix

nodes2 <- read.csv("Dataset2-Media-User-Example-NODES.csv", header=T, as.is=T)

links2 <- read.csv("Dataset2-Media-User-Example-EDGES.csv", header=T, row.names=1)

Examine the data

head(nodes2)

##    id   media media.type media.name audience.size
## 1 s01     NYT          1  Newspaper            20
## 2 s02    WaPo          1  Newspaper            25
## 3 s03     WSJ          1  Newspaper            30
## 4 s04    USAT          1  Newspaper            32
## 5 s05 LATimes          1  Newspaper            20
## 6 s06     CNN          2         TV            56

head(links2)

##     U01 U02 U03 U04 U05 U06 U07 U08 U09 U10 U11 U12 U13 U14 U15 U16 U17
## s01   1   1   1   0   0   0   0   0   0   0   0   0   0   0   0   0   0
## s02   0   0   0   1   1   0   0   0   0   0   0   0   0   0   0   0   0
## s03   0   0   0   0   0   1   1   1   1   0   0   0   0   0   0   0   0
## s04   0   0   0   0   0   0   0   0   1   1   1   0   0   0   0   0   0
## s05   0   0   0   0   0   0   0   0   0   0   1   1   1   0   0   0   0
## s06   0   0   0   0   0   0   0   0   0   0   0   0   1   1   0   0   1
##     U18 U19 U20
## s01   0   0   0
## s02   0   0   1
## s03   0   0   0
## s04   0   0   0
## s05   0   0   0
## s06   0   0   0

We can see that links2 is an adjacency matrix for a two-mode network:

links2 <- as.matrix(links2)

dim(links2)

## [1] 10 20

dim(nodes2)

## [1] 30  5

4. Turning networks into igraph objects

We start by converting the raw data to an igraph network object. Here we use igraph’s graph.data.frame function, which takes two data frames: d and vertices.

d describes the edges of the network. Its first two columns are the IDs of the source and the target node for each edge. The following columns are edge attributes (weight, type, label, or anything else). vertices starts with a column of node IDs. Any following columns are interpreted as node attributes.

Dataset 1

library(igraph)



net <- graph_from_data_frame(d=links, vertices=nodes, directed=T) 

class(net)

## [1] "igraph"

net

## IGRAPH 584921d DNW- 17 49 -- 
## + attr: name (v/c), media (v/c), media.type (v/n), type.label
## | (v/c), audience.size (v/n), type (e/c), weight (e/n)
## + edges from 584921d (vertex names):
##  [1] s01->s02 s01->s03 s01->s04 s01->s15 s02->s01 s02->s03 s02->s09
##  [8] s02->s10 s03->s01 s03->s04 s03->s05 s03->s08 s03->s10 s03->s11
## [15] s03->s12 s04->s03 s04->s06 s04->s11 s04->s12 s04->s17 s05->s01
## [22] s05->s02 s05->s09 s05->s15 s06->s06 s06->s16 s06->s17 s07->s03
## [29] s07->s08 s07->s10 s07->s14 s08->s03 s08->s07 s08->s09 s09->s10
## [36] s10->s03 s12->s06 s12->s13 s12->s14 s13->s12 s13->s17 s14->s11
## [43] s14->s13 s15->s01 s15->s04 s15->s06 s16->s06 s16->s17 s17->s04

We also have easy access to nodes, edges, and their attributes with:

E(net)       # The edges of the "net" object

## + 49/49 edges from 584921d (vertex names):
##  [1] s01->s02 s01->s03 s01->s04 s01->s15 s02->s01 s02->s03 s02->s09
##  [8] s02->s10 s03->s01 s03->s04 s03->s05 s03->s08 s03->s10 s03->s11
## [15] s03->s12 s04->s03 s04->s06 s04->s11 s04->s12 s04->s17 s05->s01
## [22] s05->s02 s05->s09 s05->s15 s06->s06 s06->s16 s06->s17 s07->s03
## [29] s07->s08 s07->s10 s07->s14 s08->s03 s08->s07 s08->s09 s09->s10
## [36] s10->s03 s12->s06 s12->s13 s12->s14 s13->s12 s13->s17 s14->s11
## [43] s14->s13 s15->s01 s15->s04 s15->s06 s16->s06 s16->s17 s17->s04

V(net)       # The vertices of the "net" object

## + 17/17 vertices, named, from 584921d:
##  [1] s01 s02 s03 s04 s05 s06 s07 s08 s09 s10 s11 s12 s13 s14 s15 s16 s17

E(net)$type  # Edge attribute "type"

##  [1] "hyperlink" "hyperlink" "hyperlink" "mention"   "hyperlink"
##  [6] "hyperlink" "hyperlink" "hyperlink" "hyperlink" "hyperlink"
## [11] "hyperlink" "hyperlink" "mention"   "hyperlink" "hyperlink"
## [16] "hyperlink" "mention"   "mention"   "hyperlink" "mention"  
## [21] "mention"   "hyperlink" "hyperlink" "mention"   "hyperlink"
## [26] "hyperlink" "mention"   "mention"   "mention"   "hyperlink"
## [31] "mention"   "hyperlink" "mention"   "mention"   "mention"  
## [36] "hyperlink" "mention"   "hyperlink" "mention"   "hyperlink"
## [41] "mention"   "mention"   "mention"   "hyperlink" "hyperlink"
## [46] "hyperlink" "hyperlink" "mention"   "hyperlink"

V(net)$media # Vertex attribute "media"

##  [1] "NY Times"            "Washington Post"     "Wall Street Journal"
##  [4] "USA Today"           "LA Times"            "New York Post"      
##  [7] "CNN"                 "MSNBC"               "FOX News"           
## [10] "ABC"                 "BBC"                 "Yahoo News"         
## [13] "Google News"         "Reuters.com"         "NYTimes.com"        
## [16] "WashingtonPost.com"  "AOL.com"

Now that we have our igraph network object, let’s make a first attempt to plot it.

plot(net, edge.arrow.size=.4,vertex.label=NA)

That doesn’t look very good. Let’s start fixing things by removing the loops in the graph.

net <- simplify(net, remove.multiple = F, remove.loops = T) 

You can extract the edge list or matrix

as_edgelist(net, names=T)

##       [,1]  [,2] 
##  [1,] "s01" "s02"
##  [2,] "s01" "s03"
##  [3,] "s01" "s04"
##  [4,] "s01" "s15"
##  [5,] "s02" "s01"
##  [6,] "s02" "s03"
##  [7,] "s02" "s09"
##  [8,] "s02" "s10"
##  [9,] "s03" "s01"
## [10,] "s03" "s04"
## [11,] "s03" "s05"
## [12,] "s03" "s08"
## [13,] "s03" "s10"
## [14,] "s03" "s11"
## [15,] "s03" "s12"
## [16,] "s04" "s03"
## [17,] "s04" "s06"
## [18,] "s04" "s11"
## [19,] "s04" "s12"
## [20,] "s04" "s17"
## [21,] "s05" "s01"
## [22,] "s05" "s02"
## [23,] "s05" "s09"
## [24,] "s05" "s15"
## [25,] "s06" "s16"
## [26,] "s06" "s17"
## [27,] "s07" "s03"
## [28,] "s07" "s08"
## [29,] "s07" "s10"
## [30,] "s07" "s14"
## [31,] "s08" "s03"
## [32,] "s08" "s07"
## [33,] "s08" "s09"
## [34,] "s09" "s10"
## [35,] "s10" "s03"
## [36,] "s12" "s06"
## [37,] "s12" "s13"
## [38,] "s12" "s14"
## [39,] "s13" "s12"
## [40,] "s13" "s17"
## [41,] "s14" "s11"
## [42,] "s14" "s13"
## [43,] "s15" "s01"
## [44,] "s15" "s04"
## [45,] "s15" "s06"
## [46,] "s16" "s06"
## [47,] "s16" "s17"
## [48,] "s17" "s04"

as_adjacency_matrix(net, attr="weight")

## 17 x 17 sparse Matrix of class "dgCMatrix"

##    [[ suppressing 17 column names 's01', 's02', 's03' ... ]]

##                                                      
## s01  . 22 22 21 .  .  .  .  .  .  .  .  .  . 20  .  .
## s02 23  . 21  . .  .  .  .  1  5  .  .  .  .  .  .  .
## s03 21  .  . 22 1  .  .  4  .  2  1  1  .  .  .  .  .
## s04  .  . 23  . .  1  .  .  .  . 22  3  .  .  .  .  2
## s05  1 21  .  . .  .  .  .  2  .  .  .  .  . 21  .  .
## s06  .  .  .  . .  .  .  .  .  .  .  .  .  .  . 21 21
## s07  .  .  1  . .  .  . 22  . 21  .  .  .  4  .  .  .
## s08  .  .  2  . .  . 21  . 23  .  .  .  .  .  .  .  .
## s09  .  .  .  . .  .  .  .  . 21  .  .  .  .  .  .  .
## s10  .  .  2  . .  .  .  .  .  .  .  .  .  .  .  .  .
## s11  .  .  .  . .  .  .  .  .  .  .  .  .  .  .  .  .
## s12  .  .  .  . .  2  .  .  .  .  .  . 22 22  .  .  .
## s13  .  .  .  . .  .  .  .  .  .  . 21  .  .  .  .  1
## s14  .  .  .  . .  .  .  .  .  .  1  . 21  .  .  .  .
## s15 22  .  .  1 .  4  .  .  .  .  .  .  .  .  .  .  .
## s16  .  .  .  . . 23  .  .  .  .  .  .  .  .  .  . 21
## s17  .  .  .  4 .  .  .  .  .  .  .  .  .  .  .  .  .

Or data frames describing nodes and edges:

as_data_frame(net, what="edges")

##    from  to      type weight
## 1   s01 s02 hyperlink     22
## 2   s01 s03 hyperlink     22
## 3   s01 s04 hyperlink     21
## 4   s01 s15   mention     20
## 5   s02 s01 hyperlink     23
## 6   s02 s03 hyperlink     21
## 7   s02 s09 hyperlink      1
## 8   s02 s10 hyperlink      5
## 9   s03 s01 hyperlink     21
## 10  s03 s04 hyperlink     22
## 11  s03 s05 hyperlink      1
## 12  s03 s08 hyperlink      4
## 13  s03 s10   mention      2
## 14  s03 s11 hyperlink      1
## 15  s03 s12 hyperlink      1
## 16  s04 s03 hyperlink     23
## 17  s04 s06   mention      1
## 18  s04 s11   mention     22
## 19  s04 s12 hyperlink      3
## 20  s04 s17   mention      2
## 21  s05 s01   mention      1
## 22  s05 s02 hyperlink     21
## 23  s05 s09 hyperlink      2
## 24  s05 s15   mention     21
## 25  s06 s16 hyperlink     21
## 26  s06 s17   mention     21
## 27  s07 s03   mention      1
## 28  s07 s08   mention     22
## 29  s07 s10 hyperlink     21
## 30  s07 s14   mention      4
## 31  s08 s03 hyperlink      2
## 32  s08 s07   mention     21
## 33  s08 s09   mention     23
## 34  s09 s10   mention     21
## 35  s10 s03 hyperlink      2
## 36  s12 s06   mention      2
## 37  s12 s13 hyperlink     22
## 38  s12 s14   mention     22
## 39  s13 s12 hyperlink     21
## 40  s13 s17   mention      1
## 41  s14 s11   mention      1
## 42  s14 s13   mention     21
## 43  s15 s01 hyperlink     22
## 44  s15 s04 hyperlink      1
## 45  s15 s06 hyperlink      4
## 46  s16 s06 hyperlink     23
## 47  s16 s17   mention     21
## 48  s17 s04 hyperlink      4

as_data_frame(net, what="vertices")

##     name               media media.type type.label audience.size
## s01  s01            NY Times          1  Newspaper            20
## s02  s02     Washington Post          1  Newspaper            25
## s03  s03 Wall Street Journal          1  Newspaper            30
## s04  s04           USA Today          1  Newspaper            32
## s05  s05            LA Times          1  Newspaper            20
## s06  s06       New York Post          1  Newspaper            50
## s07  s07                 CNN          2         TV            56
## s08  s08               MSNBC          2         TV            34
## s09  s09            FOX News          2         TV            60
## s10  s10                 ABC          2         TV            23
## s11  s11                 BBC          2         TV            34
## s12  s12          Yahoo News          3     Online            33
## s13  s13         Google News          3     Online            23
## s14  s14         Reuters.com          3     Online            12
## s15  s15         NYTimes.com          3     Online            24
## s16  s16  WashingtonPost.com          3     Online            28
## s17  s17             AOL.com          3     Online            33

Datsset 2

I thinkk this is the one for Real Estate

As we have seen above, this time the edges of the network are in a matrix format. We can read those into a graph object using graph_from_incidence_matrix()

head(nodes2)

##    id   media media.type media.name audience.size
## 1 s01     NYT          1  Newspaper            20
## 2 s02    WaPo          1  Newspaper            25
## 3 s03     WSJ          1  Newspaper            30
## 4 s04    USAT          1  Newspaper            32
## 5 s05 LATimes          1  Newspaper            20
## 6 s06     CNN          2         TV            56

head(links2)

##     U01 U02 U03 U04 U05 U06 U07 U08 U09 U10 U11 U12 U13 U14 U15 U16 U17
## s01   1   1   1   0   0   0   0   0   0   0   0   0   0   0   0   0   0
## s02   0   0   0   1   1   0   0   0   0   0   0   0   0   0   0   0   0
## s03   0   0   0   0   0   1   1   1   1   0   0   0   0   0   0   0   0
## s04   0   0   0   0   0   0   0   0   1   1   1   0   0   0   0   0   0
## s05   0   0   0   0   0   0   0   0   0   0   1   1   1   0   0   0   0
## s06   0   0   0   0   0   0   0   0   0   0   0   0   1   1   0   0   1
##     U18 U19 U20
## s01   0   0   0
## s02   0   0   1
## s03   0   0   0
## s04   0   0   0
## s05   0   0   0
## s06   0   0   0

net2<- graph_from_incidence_matrix(links2)

table(V(net2)$type)

## 
## FALSE  TRUE 
##    10    20

To transform a one-mode network matrix into an igraph object, use instead graph_from_adjacency_matrix().

net2.bp <- bipartite.projection(net2)

We can calculate the projections manually as well:

   as_incidence_matrix(net2)  %*% t(as_incidence_matrix(net2)) 

##     s01 s02 s03 s04 s05 s06 s07 s08 s09 s10
## s01   3   0   0   0   0   0   0   0   0   1
## s02   0   3   0   0   0   0   0   0   1   0
## s03   0   0   4   1   0   0   0   0   1   0
## s04   0   0   1   3   1   0   0   0   0   1
## s05   0   0   0   1   3   1   0   0   0   1
## s06   0   0   0   0   1   3   1   1   0   0
## s07   0   0   0   0   0   1   3   1   0   0
## s08   0   0   0   0   0   1   1   4   1   0
## s09   0   1   1   0   0   0   0   1   3   0
## s10   1   0   0   1   1   0   0   0   0   2

 t(as_incidence_matrix(net2)) %*%   as_incidence_matrix(net2)

##     U01 U02 U03 U04 U05 U06 U07 U08 U09 U10 U11 U12 U13 U14 U15 U16 U17
## U01   2   1   1   0   0   0   0   0   0   0   1   0   0   0   0   0   0
## U02   1   1   1   0   0   0   0   0   0   0   0   0   0   0   0   0   0
## U03   1   1   1   0   0   0   0   0   0   0   0   0   0   0   0   0   0
## U04   0   0   0   1   1   0   0   0   0   0   0   0   0   0   0   0   0
## U05   0   0   0   1   1   0   0   0   0   0   0   0   0   0   0   0   0
## U06   0   0   0   0   0   2   1   1   1   0   0   0   0   0   0   0   0
## U07   0   0   0   0   0   1   1   1   1   0   0   0   0   0   0   0   0
## U08   0   0   0   0   0   1   1   1   1   0   0   0   0   0   0   0   0
## U09   0   0   0   0   0   1   1   1   2   1   1   0   0   0   0   0   0
## U10   0   0   0   0   0   0   0   0   1   1   1   0   0   0   0   0   0
## U11   1   0   0   0   0   0   0   0   1   1   3   1   1   0   0   0   0
## U12   0   0   0   0   0   0   0   0   0   0   1   1   1   0   0   0   0
## U13   0   0   0   0   0   0   0   0   0   0   1   1   2   1   0   0   1
## U14   0   0   0   0   0   0   0   0   0   0   0   0   1   2   1   1   1
## U15   0   0   0   0   0   0   0   0   0   0   0   0   0   1   1   1   0
## U16   0   0   0   0   0   0   0   0   0   0   0   0   0   1   1   2   1
## U17   0   0   0   0   0   0   0   0   0   0   0   0   1   1   0   1   2
## U18   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   1   1
## U19   0   0   0   0   0   1   0   0   0   0   0   0   0   0   0   1   1
## U20   0   0   0   1   1   1   0   0   0   0   0   0   0   0   0   0   0
##     U18 U19 U20
## U01   0   0   0
## U02   0   0   0
## U03   0   0   0
## U04   0   0   1
## U05   0   0   1
## U06   0   1   1
## U07   0   0   0
## U08   0   0   0
## U09   0   0   0
## U10   0   0   0
## U11   0   0   0
## U12   0   0   0
## U13   0   0   0
## U14   0   0   0
## U15   0   0   0
## U16   1   1   0
## U17   1   1   0
## U18   1   1   0
## U19   1   2   1
## U20   0   1   2

plot(net2.bp$proj1, vertex.label.color="black", vertex.label.dist=1,

     vertex.size=7, vertex.label=nodes2$media[!is.na(nodes2$media.type)])

plot(net2.bp$proj2, vertex.label.color="black", vertex.label.dist=1,

     vertex.size=7, vertex.label=nodes2$media[ is.na(nodes2$media.type)])

5. Plotting networks with igraph

I graph plotting parameters partial list: NODES
vertex.color Node color vertex.frame.color Node border color vertex.shape One of “none”, “circle”, “square”, “csquare”, “rectangle” “crectangle”, “vrectangle”, “pie”, “raster”, or “sphere” vertex.size Size of the node (default is 15) vertex.size2 The second size of the node (e.g. for a rectangle) vertex.label Character vector used to label the nodes vertex.label.family Font family of the label (e.g.“Times”, “Helvetica”) vertex.label.font Font: 1 plain, 2 bold, 3, italic, 4 bold italic, 5 symbol vertex.label.cex Font size (multiplication factor, device-dependent) vertex.label.dist Distance between the label and the vertex vertex.label.degree The position of the label in relation to the vertex, where 0 right, “pi” is left, “pi/2” is below, and “-pi/2” is above EDGES
edge.color Edge color edge.width Edge width, defaults to 1 edge.arrow.size Arrow size, defaults to 1 edge.arrow.width Arrow width, defaults to 1 edge.lty Line type, could be 0 or “blank”, 1 or “solid”, 2 or “dashed”, 3 or “dotted”, 4 or “dotdash”, 5 or “longdash”, 6 or “twodash” edge.label Character vector used to label edges edge.label.family Font family of the label (e.g.“Times”, “Helvetica”) edge.label.font Font: 1 plain, 2 bold, 3, italic, 4 bold italic, 5 symbol edge.label.cex Font size for edge labels edge.curved Edge curvature, range 0-1 (FALSE sets it to 0, TRUE to 0.5) arrow.mode Vector specifying whether edges should have arrows, possible values: 0 no arrow, 1 back, 2 forward, 3 both OTHER
margin Empty space margins around the plot, vector with length 4 frame if TRUE, the plot will be framed main If set, adds a title to the plot sub If set, adds a subtitle to the plot

We can set the node & edge options in two ways - the first one is to specify them in the plot() function, as we are doing below.

# Plot with curved edges (edge.curved=.1) and reduce arrow size:

plot(net, edge.arrow.size=.4, edge.curved=.1)

Set Edge Color

Set Vertex color

# Set edge color to gray, and the node color to orange. 

# Replace the vertex label with the node names stored in "media"

plot(net, edge.arrow.size=.2, edge.curved=0,

     vertex.color="orange", vertex.frame.color="#555555",

     vertex.label=V(net)$media, vertex.label.color="black",

     vertex.label.cex=.7) 

THIS IS JUST WHAT i WANT TO DO

The second way to set attributes is to add them to the igraph object. Let’s say we want to color our network nodes based on type of media, and size them based on audience size (larger audience -> larger node). We will also change the width of the edges based on their weight.

# Generate colors based on media type:

colrs <- c("gray50", "tomato", "gold")

V(net)$color <- colrs[V(net)$media.type]

# Set node size based on audience size:

V(net)$size <- V(net)$audience.size*0.7



# The labels are currently node IDs.

# Setting them to NA will render no labels:

V(net)$label.color <- "black"

V(net)$label <- NA



# Set edge width based on weight:

E(net)$width <- E(net)$weight/6



#change arrow size and edge color:

E(net)$arrow.size <- .2

E(net)$edge.color <- "gray80"



E(net)$width <- 1+E(net)$weight/12

plot(net)

We can also override the attributes explicitly in the plot:

plot(net, edge.color="orange", vertex.color="gray50") 

It helps to add a legend explaining the meaning of the colors we used:

plot(net) 

legend(x=-1.5, y=-1.1, c("Newspaper","Television", "Online News"), pch=21,

       col="#777777", pt.bg=colrs, pt.cex=2, cex=.8, bty="n", ncol=1)

Sometimes, especially with semantic networks, we may be interested in plotting only the labels of the nodes:

plot(net, vertex.shape="none", vertex.label=V(net)$media, 

     vertex.label.font=2, vertex.label.color="gray40",

     vertex.label.cex=.7, edge.color="gray85")

plot edge color to match starting node

edge.start <- ends(net, es=E(net), names=F)[,1]

edge.col <- V(net)$color[edge.start]



plot(net, edge.color=edge.col, edge.curved=.1) 

5.2 Network layouts

We use the sample_pa() function which generates a simple graph starting from one node and adding more nodes and links based on a preset level of preferential attachment (Barabasi-Albert model).

net.bg <- sample_pa(80) 

V(net.bg)$size <- 8

V(net.bg)$frame.color <- "white"

V(net.bg)$color <- "orange"

V(net.bg)$label <- "" 

E(net.bg)$arrow.mode <- 0

plot(net.bg)

Control layout

plot(net.bg, layout=layout_randomly)

Circular

l <- layout_in_circle(net.bg)

plot(net.bg, layout=l)

l <- cbind(1:vcount(net.bg), c(1, vcount(net.bg):2))

plot(net.bg, layout=l)

Circle

l <- layout_in_circle(net.bg)

plot(net.bg, layout=l)

Sphere

l <- layout_on_sphere(net.bg)

plot(net.bg, layout=l)

Fruchterman-Reingold is one of the most used force-directed layout algorithms out there.

Force-directed layouts try to get a nice-looking graph where edges are similar in length and cross each other as little as possible. They simulate the graph as a physical system. Nodes are electrically charged particles that repulse each other when they get too close. The edges act as springs that attract connected nodes closer together. As a result, nodes are evenly distributed through the chart area, and the layout is intuitive in that nodes which share more connections are closer to each other. The disadvantage of these algorithms is that they are rather slow and therefore less often used in graphs larger than ~1000 vertices. You can set the “weight” parameter which increases the attraction forces among nodes connected by heavier edges.

l <- layout_with_fr(net.bg)

plot(net.bg, layout=l)

Another popular force-directed algorithm that produces nice results for connected graphs is Kamada Kawai. Like Fruchterman Reingold, it attempts to minimize the energy in a spring system.

l <- layout_with_kk(net.bg)

plot(net.bg, layout=l)

Let’s take a look at all available layouts in igraph:

layouts <- grep("^layout_", ls("package:igraph"), value=TRUE)[-1] 

# Remove layouts that do not apply to our graph.

layouts <- layouts[!grepl("bipartite|merge|norm|sugiyama|tree", layouts)]



par(mfrow=c(3,3), mar=c(1,1,1,1))

for (layout in layouts) {

  print(layout)

  l <- do.call(layout, list(net)) 

  plot(net, edge.arrow.mode=0, layout=l, main=layout) }

## [1] "layout_as_star"

## [1] "layout_components"

## [1] "layout_in_circle"

## [1] "layout_nicely"

## [1] "layout_on_grid"

## [1] "layout_on_sphere"

## [1] "layout_randomly"

## [1] "layout_with_dh"

## [1] "layout_with_drl"

## [1] "layout_with_fr"

## [1] "layout_with_gem"

## [1] "layout_with_graphopt"

## [1] "layout_with_kk"

## [1] "layout_with_lgl"

## [1] "layout_with_mds"

There are more sophisticated ways to extract the key edges, but for the purposes of this exercise we’ll only keep ones that have weight higher than the mean for the network. In igraph, we can delete edges using delete_edges(net, edges):

cut.off <- mean(links$weight) 

net.sp <- delete_edges(net, E(net)[weight<cut.off])

plot(net.sp) 

5.3 Improving network plots

Notice that our network plot is still not too helpful. We can identify the type and size of nodes, but cannot see much about the structure since the links we’re examining are so dense. One way to approach this is to see if we can sparsify the network, keeping only the most important ties and discarding the rest.

There are more sophisticated ways to extract the key edges, but for the purposes of this exercise we’ll only keep ones that have weight higher than the mean for the network. In igraph, we can delete edges using delete_edges(net, edges):

hist(links$weight)

mean(links$weight)

## [1] 12.40816

sd(links$weight)

## [1] 9.905635

cut.off <- mean(links$weight) 

net.sp <- delete_edges(net, E(net)[weight<cut.off])
#      Above drops Edgeswhose weight is < mean.
plot(net.sp) 

Plotting multipole.

Here the data are split side by side.

net.m <- net - E(net)[E(net)$type=="hyperlink"] # another way to delete edges

net.h <- net - E(net)[E(net)$type=="mention"]



# Plot the two links separately:

par(mfrow=c(1,2))

plot(net.h, vertex.color="orange", main="Tie: Hyperlink")

plot(net.m, vertex.color="lightsteelblue2", main="Tie: Mention")

make sure the plots stay in place

l <- layout_with_fr(net)

plot(net.h, vertex.color="orange", layout=l, main="Tie: Hyperlink")

plot(net.m, vertex.color="lightsteelblue2", layout=l, main="Tie: Mention")

# in a seperate cell after the graph:
dev.off()

## null device 
##           1

5.4 Interactive plotting with tkplot

I skipped this.

5.5 Other ways to represent a network

For example, here is a quick heatmap of the network matrix:

netm <- get.adjacency(net, attr="weight", sparse=F)

colnames(netm) <- V(net)$media

rownames(netm) <- V(net)$media



palf <- colorRampPalette(c("gold", "dark orange")) 

heatmap(netm[,17:1], Rowv = NA, Colv = NA, col = palf(100), 

        scale="none", margins=c(10,10) )

Using text as nodes may be helpful at times:

plot(net2, vertex.shape="none", vertex.label=nodes2$media,

     vertex.label.color=V(net2)$color, vertex.label.font=2.5, 

     vertex.label.cex=.6, edge.color="gray70",  edge.width=2)

#### 6.Network and node descriptives 6.1 Density

6.2 Reciprocity

6.3 Transitivity

6.4 Diameter

6.5 Node degrees

8. Subgroups and communities Cliques

COMMUNITY DETECTION

8.2 Community detection

ceb <- cluster_edge_betweenness(net) 

dendPlot(ceb, mode="hclust")

Network plot detected communities

plot(ceb, net)