# Basic Network Analysis and Visualization for Directed Graphs in R

networks
centrality

Choosing the Right Centrality Measure.

Author

Jacob Jameson

Published

November 1, 2022

## Social Network Analysis?

“There is certainly no unanimity on exactly what centrality is or on its conceptual foundations, and there is little agreement on the proper procedure for its measurement.” - Linton Freeman (1977)

Social network analysis can be used to measure the importance of a person as a function of the social structure of a community or organization. This post uses visualization as a tool to explain how different measures of centrality may be used to analyze different questions in a network analysis. In these examples we will be specifically looking at directed graphs to compare the following centrality measures and their use-cases:

• Degree Centrality
• Betweenness Centrality
• Eigenvector Centrality
• Katz Centrality
• HITS Hubs and Authorities

An example of a directed graph would be one in which people nominate their top 2 friends. In this graph, nodes (people) would connect to others nodes through directed edges (nominations). It is possible for Jacob to nominate Jenna without Jenna nominating him back. You can imagine why centrality in a friendship network might take into account the direction of these nominations. If I list 100 people as my friends and none of them list me back, do we think I am a popular person?

## Simulate Our Data

For our simulated data we are going to be looking at a classroom that contains 14 male and 14 female students. Suppose that each student was asked to name their top 2 male and top 2 female friends in the class. We are interested in analyzing a slew of different research questions where the centrality of student in the class may be of importance.

Let’s create the data:

library(tidyverse)

We begin with a 4 vectors: all males in the classroom, all females in the classroom, a probability distribution for selecting friends of the same sex, a probability distribution for selecting friends of the opposite sex.

males <- c('Jacob', 'Louis', 'Chris', 'Wyatt', 'Nolan', 'Robert',
'Zach', 'John','Bob', 'David', 'Avery', 'Ronald',
'Dallas', 'Dylan')

females <- c('Bohan', 'Jenna', 'Katarina', 'Hassina', 'Towo',
'Becca', 'Meredith', 'Gracie', 'Kayla', 'Marlene',
'Jade', 'Allyssa', 'Reigne', 'Wendy')

probs.diff.sex = c(0.15,0.15,0.05,0.05,0.1,0.1,0.05,0.1,0.01,0.09,0.05,0.05,0.025,0.025)
probs.same.sex = c(0.15,0.15,0.05,0.05,0.1,0.1,0.05,0.1,0.01,0.09,0.05,0.05,0.05)

The function below, simulate.top.friends, will produce a dataframe that will contain each student’s picks for their top 2 male and top 2 female friends in the classroom.

set.seed(1997)

simulate.top.friends <- function(males, females, probs.diff.sex, probs.same.sex) {

dat <- setNames(data.frame(matrix(ncol = 6, nrow = 0)),
c("Ego", "Ego Sex", "MF1", "MF2", "FF1", "FF2"))

for (ego in males) {
temp.males <- males[! males %in% ego]

male.friends.i <- sample.int(13, 2, replace = FALSE, prob = probs.same.sex)
female.friends.i <- sample.int(14, 2, replace = FALSE, prob = probs.diff.sex)

male.friend.1 <- temp.males[male.friends.i[1]]
male.friend.2 <- temp.males[male.friends.i[2]]

female.friend.1 <- females[female.friends.i[1]]
female.friend.2 <- females[female.friends.i[2]]

dat[nrow(dat) + 1,] = c(ego, 'Male', male.friend.1, male.friend.2,
female.friend.1, female.friend.2)

}
for (ego in females) {
temp.females <- females[! females %in% ego]

male.friends.i <- sample.int(14, 2, replace = FALSE, prob = probs.diff.sex)
female.friends.i <- sample.int(13, 2, replace = FALSE, prob = probs.same.sex)

male.friend.1 <- males[male.friends.i[1]]
male.friend.2 <- males[male.friends.i[2]]

female.friend.1 <- temp.females[female.friends.i[1]]
female.friend.2 <- temp.females[female.friends.i[2]]

dat[nrow(dat) + 1,] = c(ego, 'Female', male.friend.1, male.friend.2,
female.friend.1, female.friend.2)

}
return(dat)
}

Let’s take a look at our friendship data that we will be working with!

simulate.top.friends(males,females, probs.diff.sex, probs.same.sex)
Ego Ego Sex MF1 MF2 FF1 FF2
Jacob Male Chris Avery Katarina Meredith
Louis Male Jacob Avery Towo Hassina
Chris Male Jacob Robert Gracie Becca
Wyatt Male Nolan Jacob Marlene Jenna
Nolan Male Jacob Bob Bohan Towo
Robert Male Nolan Louis Jenna Bohan
Zach Male Jacob Avery Meredith Towo
John Male Bob Louis Bohan Wendy
Bob Male Avery Jacob Jenna Wendy
David Male Wyatt Louis Becca Jenna
Avery Male Louis Jacob Jenna Becca
Ronald Male Jacob Robert Becca Wendy
Dallas Male Robert Jacob Bohan Jenna
Dylan Male Jacob David Towo Marlene
Bohan Female Robert Nolan Allyssa Kayla
Jenna Female David Bob Katarina Meredith
Katarina Female David Nolan Gracie Reigne
Hassina Female Robert David Becca Jade
Towo Female John Nolan Jade Jenna
Becca Female Nolan Dallas Kayla Towo
Meredith Female Louis Jacob Towo Bohan
Gracie Female Jacob Ronald Bohan Hassina
Kayla Female Ronald Louis Bohan Gracie
Marlene Female John Robert Hassina Towo
Jade Female Ronald Louis Hassina Bohan
Allyssa Female Avery Zach Gracie Bohan
Reigne Female John Zach Bohan Hassina
Wendy Female Nolan Jacob Towo Bohan

## Creating a Graphing Object

There are many available centrality measures that have been developed for network analysis. At this time, there are no packages that are so comprehensive that it includes all of the measures. I will, therefore, limit this discussion to a subset of the measures that are included in igraph.

igraph has a really great function that allows us to turn a dataframe into an igraph object. However, the function requires our data to be in “long” form so we will need to do some reshaping. Let’s restructure our data such that each row represents one directed friend nomination. We are going to call this “Source-Target Form”.

library(reshape)

friendships <- melt(simulate.top.friends(males,females,probs.diff.sex, probs.same.sex),
id=c("Ego", "Ego Sex")) %>%
select(source=Ego, source_sex =Ego Sex, target=value) %>%
arrange(source)

Let’s look at the first 10 observations so we can understand the format needed to turn this data into an igraph object.

head(friendships, 10)
source source_sex target
Allyssa Female David
Allyssa Female Nolan
Allyssa Female Jenna
Allyssa Female Towo
Avery Male John
Avery Male Chris
Avery Male Allyssa
Avery Male Meredith
Becca Female Chris
Becca Female Wyatt

We will use the graph_from_data_frame function to create the igraph object.

library(igraph)
network <- graph_from_data_frame(friendships[,c('source','target','source_sex')],
directed = TRUE)

network
IGRAPH 006a09e DN-- 28 112 --
+ attr: name (v/c), source_sex (e/c)
+ edges from 006a09e (vertex names):
[1] Allyssa->David    Allyssa->Nolan    Allyssa->Jenna    Allyssa->Towo
[5] Avery  ->John     Avery  ->Chris    Avery  ->Allyssa  Avery  ->Meredith
[9] Becca  ->Chris    Becca  ->Wyatt    Becca  ->Jade     Becca  ->Jenna
[13] Bob    ->Dylan    Bob    ->David    Bob    ->Marlene  Bob    ->Wendy
[17] Bohan  ->Nolan    Bohan  ->John     Bohan  ->Katarina Bohan  ->Reigne
[21] Chris  ->Jacob    Chris  ->Avery    Chris  ->Bohan    Chris  ->Allyssa
[25] Dallas ->Louis    Dallas ->Robert   Dallas ->Jenna    Dallas ->Towo
[29] David  ->Zach     David  ->Dallas   David  ->Bohan    David  ->Jenna
+ ... omitted several edges

Let’s better understand the information contained in an igraph object:

• IGRAPH simply annotates network as an igraph object
• Whatever random six digit alphanumeric string follows IGRAPH is simply how igraph identifies the graph for itself, it’s not important for our purposes.
• D would tell us that it is directed graph
• N indicates that network is a named graph, in that the vertices have a name attribute
• – refers to attributes not applicable to network, but we will see them in the future:
• 28 refers to the number of vertices in network
• 112 refers to the number of edges in network
• attr: is a list of attributes within the graph.
• (v/c), which will appear following name, tells us that it is a vertex attribute of a character data type.
• (e/c) or (e/n) referring to edge attributes that are of character or numeric data types
• edges from arbitrary igraph name (vertex names): lists a sample of network’s edges using the names of the vertices which they connect.

Now let’s create a rough plot to look at our network!

lay <- layout_with_kk(network)

par(bg="grey98")
plot(network, layout = lay, edge.color="grey80",
vertex.color="lightblue", vertex.label.color = "black")

For the rest of this post, we are going to talk about a few different measures of centrality, what they capture mathematically and intuitively, and we will look at plots where the size of the node corresponds to the relative centrality score.

## Measures of Centrality

### Degree Centrality

For directed graphs, in-degree, or number of incoming points, is one way we can determine the importance factor for nodes. The Degree of a node is the number of edges that it has. The basic intuition is that, nodes with more connections are more influential and important in a network. In other words, the people with more friend nominations in our simulated social network are the ones with greater importance according to this metric.

Degree.Directed <- degree(network)
Indegree <- degree(network, mode="in")
Outdegree <- degree(network, mode="out")

CompareDegree <- cbind(Degree.Directed, Indegree, Outdegree)
head(CompareDegree, 10)
Degree.Directed Indegree Outdegree
Allyssa 7 3 4
Avery 7 3 4
Becca 7 3 4
Bob 7 3 4
Bohan 12 8 4
Chris 7 3 4
Dallas 7 3 4
David 11 7 4
Dylan 7 3 4
Gracie 5 1 4

This is a very reasonable way to measure importance within a network. If we are trying to determine who in our classroom is the most popular, we might define that as the greatest number of friendship nominations.

lay <- layout_with_kk(network)

par(bg="grey98")
plot(network, layout = lay, edge.color="grey80",
vertex.size=degree(network, mode="in")*2,  # Rescaled by multiplying by 2
main="In-Degree", vertex.label.dist=1.5,
vertex.color="lightblue", vertex.label.color = "black")

## Betweenness Centrality

Betweenness Centrality is another centrality that is based on shortest path between nodes. It is determined as number of the shortest path passing by the given node. For starting node $$s$$, destination node $$t$$ and the input node $$i$$ that holds $$s \ne t \ne i$$, let $$n_{st}^i$$ be 1 if node $$i$$ lies on the shortest path between $$s$$ and $$t$$; and $$0$$ if not. So the betweenness centrality is defined as:

$x_i = \sum_{st} n_{st}^i$ However, there can be more than one shortest path between $$s$$ and $$t$$ and that will count for centrality measure more than once. Thus, we need to divide the contribution to $$g\_{st}$$, total number of shortest paths between $$s$$ and $$t$$.

$x_i = \sum_{st} \frac{n_{st}^i}{g_{st}}$

Essentially the size of the node here represents the frequency with which that node lies on the shortest path between other nodes.

par(bg="grey98")
plot(network, layout=lay,
vertex.label.dist=1.5, vertex.label.color = "black", edge.color="grey80",
vertex.size=betweenness(network)*0.25,  # Rescaled by multiplying by 0.25
main="Betweenness Centrality", vertex.color="lightblue")

## Eigenvector Centrality

Eigenvector centrality is a basic extension of degree centrality, which defines centrality of a node as proportional to its neighbors’ importance. When we sum up all connections of a node, not all neighbors are equally important. This is a very interesting way to measure popularity in our classroom where we say the popularity of your friends matters more than the number of friends. Let’s consider two nodes in a friend network with same degree, the one who is connected to more central nodes should be more central.

First, we define an initial guess for the centrality of nodes in a graph as $$x_i=1$$. Now we are going to iterate for the new centrality value $$x_i'$$ for node $$i$$ as following:

$x_i' = \sum_{j} A_{ij}x_j$

Here $$A_{ij}$$ is an element of the adjacency matrix, where it gives 1 or 0 for whether an edge exists between nodes $$i$$ and $$j$$. it can also be written in matrix notation as $$\mathbf{x'} = \mathbf{Ax}$$.

We iterate over t steps to find the vector $$\mathbf{x}(t)$$ as:

$\mathbf{x}(t) = \mathbf{A^t x}(0)$

par(bg="grey98")
plot(network, layout=lay,
vertex.label.dist=1.5, vertex.label.color = "black", edge.color="grey80",
vertex.size=evcent(network)$vector*15, # Rescaled by multiplying by 15 main="Eigenvector Centrality", vertex.color="lightblue") The plot shows, the students which have the same number of friend nominations are not necessarily in the same size. The one that is connected to more central, or “popular” nodes are larger in this visualization. However, as we can see from the definition, this can be a problematic measure for directed graphs. Let’s say that a student who received no friend nominations themselves nominates another student as a friend. Because that person has 0 friend nominations themselves, they would not contribute any importance to the person they nominated. In other words, eigenvector centrality would not take zero in-degree nodes into account in directed graphs. However there is a solution to this! ## Katz Centrality Katz centrality introduces two positive constants $$\alpha$$ and $$\beta$$ to tackle the problem of eigenvector centrality with zero in-degree nodes: $x_i = \alpha \sum_{j} A_{ij} x_j + \beta$, again $$A_{ij}$$ is an element of the adjacency matrix, and it can also be written in matrix notation as $$\mathbf{x} = \alpha \mathbf{Ax} + \beta \mathbf{1}$$. This $$\beta$$ constant gives a free centrality contribution for all nodes even though they don’t get any contribution from other nodes. The existence of a node alone would provide it some importance. $$\alpha$$ constant determines the balances between the contribution from other nodes and the free constant. Unfortunately, igraph does not have a function to compute Katz centrality, so we will need to do it the old fashioned way. katz.centrality = function(g, alpha, beta, t) { n = vcount(g); A = get.adjacency(g); x0 = rep(0, n); x1 = rep(1/n, n); eps = 1/10^t; iter = 0; while (sum(abs(x0 - x1)) > eps) { x0 = x1; x1 = as.vector(alpha * x1 %*% A) + beta; iter = iter + 1; } return(list(aid = x0, vector = x1, iter = iter)) } par(bg="grey98") plot(network, layout=lay, vertex.label.dist=1.5, vertex.label.color = "black", edge.color="grey80", vertex.size=katz.centrality(network, 0.1, 1, 0.01)$vector*5,   # Rescaled by multiplying by 15
main="Katz Centrality", vertex.color="lightblue")

Although this method is introduced as a solution for directed graphs, it can be useful for some applications of undirected graphs as well.

## HITS Hubs and Authorities

par(bg="grey98")
plot(network, layout=lay,
vertex.label.dist=1.5, vertex.label.color = "black", edge.color="grey80",
vertex.size=hub.score(network)$vector*15, # Rescaled by multiplying by 15 main="HITS Hubs", vertex.color="lightblue") par(bg="grey98") plot(network, layout=lay, vertex.label.dist=1.5 , vertex.label.color = "black", edge.color="grey80", vertex.size=authority.score(network)$vector*15,   # Rescaled by multiplying by 15
main="HITS Authorities", vertex.color="lightblue")

Up until this point, we have discussed the measures that captures high node centrality, however, there can be nodes in the network which are important for the network, but they are not central. In order to find out such nodes, the HITS algorithm introduces two types of central nodes: Hubs and Authorities. For Hubs, we might consider a node important if it links to many highly nominated nodes (i.e. the person nominates many popular people as their friends). For Authorities, we might consider a node to be of importance if many highly nominated nodes link to it (i.e. nominated by many popular people).

Authority Centrality is defined as the sum of the hub centralities which point to the node (i):

$x_i = \alpha \sum_{j} A_{ij} y_j,$

where $$\alpha$$ is constant. Likewise, Hub Centrality is the sum of the authorities which are pointed by the node $$i$$:

$y_i = \beta \sum_{j} A_{ji} x_j,$

with constant $$\beta$$. Here notice that the element of the adjacency matrix are swapped for Hub Centrality because we are concerned with outgoing edges for hubs. So in matrix notation:

$\mathbf{x} = \alpha \mathbf{Ay}, \quad$

$\mathbf{y} = \beta \mathbf{A^Tx}.$ As it can be seen from the drawing, HITS Algorithm also tackles the problem with zero in-degree nodes of Eigenvector Centrality. These zero in-degree nodes become central hubs and contribute to other nodes. Yet we can still use a free centrality contribution constant like in Katz Centrality or other variants.

Although these measures are generally used in a citation network or the internet, this can be pretty interesting in the context of our classroom frienship network. Maybe we are interested in knowing who are the non-popular students that nominate many popular students as friends, but receive no reciprocity in terms of nominations.

## Other Packages that I Like for Visualization

I want to conclude this post with some sample code to produce a nice network plot using the ggnetwork package. igraph is only one package that exists for network visualization and centrality calculations, I encourage you to check out additional packages which may be stronger than igraph for some purposes.

library(GGally)
library(network)
library(ggnetwork)

net <- list(nodes=friendships[c('source', 'target', 'source_sex')],
edges=friendships[c('source', 'target', 'source_sex')])

# create node attribute data
net.cet <- as.character(net$nodes$source_sex)
names(net.cet) = net$nodes$source
edges <- net\$edges

# create network
net.net <- edges[, c("source", "target") ]
net.net <- network::network(net.net, directed = TRUE)

# create sourc sex node attribute
net.net %v% "source_sex" <- net.cet[ network.vertex.names(net.net) ]
set.seed(1)
ggnet2(net.net, color = "source_sex",
palette = c("Female" = "purple", "Male" = "maroon"), size = 'indegree',
arrow.size = 3, arrow.gap = 0.04, alpha = 1,  label = TRUE, vjust = 2.5, label.size = 3.5,
edge.alpha = 0.5, mode = "kamadakawai",edge.color = 'grey50',
color.legend = "Student Sex") + theme_bw() + theme_blank()  +
theme(legend.position = "bottom", text = element_text(size = 15),
plot.caption = element_text(hjust = 0)) + guides(size=F) +
labs(title='Social Network Mapping of Friendship Nominations for Simulated Data',
caption = str_wrap("The size of the dots represent the number of
friendship nominations by others. The maroon dots represent males
while the purple dots represent females. Directed edges are used
to indicate the direction of friendship nominations.
Edges that are bi-directional indicated friendship reciprocity.
\n\n This particular network represents the frienship nominations
of 28 students from our simulated data.", 128))

Thank you!

Jacob