This is the second post of a series on the concept of “network centrality” with
applications in R and the package `netrankr`

. The first part briefly introduced the concept itself,
relevant R package, and some reoccurring issues for applications.
This post will discuss some theoretical foundations and common properties of indices,
specifically the *neighborhood-inclusion preorder* and what we can learn from them.

```
library(igraph)
library(ggraph)
library(tidyverse)
library(netrankr)
```

# Introduction

When looking at the vast amount of indices, it may be reasonable to ask if there is any natural limit for what can be considered a centrality index. Concretely, are there any theoretical properties that an index has to have in order to be called a centrality index? There exist several axiomatic systems for centrality, which define some desirable properties that a proper index should have. While these systems are able to shed some light on specific groups of indices, they are in most cases not comprehensive. That is, it is often possible to construct counterexamples for most indices such that they do not fulfill the properties. Instead of the rather normative axiomatic approach, we explore a more descriptive approach. We will address the following questions:

- Are there any properties that are shared by all (or almost all) indices?
- If so, can they be exploited for a different kind of centrality analysis?

# Neighborhood-inclusion

In the first post, we examined the following two small examples.

```
#data can be found here: https://github.com/schochastics/centrality_tutorial
g1 <- readRDS("example_1.rds")
g2 <- readRDS("example_2.rds")
```

It turned out that for network 1, 35 indices gave very different results and for network 2 they all coincided. In the following, we discuss why this is the case.

It turns out that there actually is a very intuitive structural property that underlies many centrality indices.
If a node has exactly the same neighbors as another and potentially some more, it will
never be less central, independent of the choice of index. Formally,
\[
N(i) \subseteq N[j] \implies c(i) \leq c(j)
\]
for centrality indices \(c\). This property is called *neighborhood-inclusion*.
(*I will spare the technical details at this point, but if you are interested in the math, please contact me.*)

An illustration is given below.
Node \(i\) and \(j\) have three common neighbors (the black nodes), but \(j\) has two additional neighbors
(the grey nodes), hence \(i\)’s neighborhood is included in the neighborhood of \(j\). Note that
the inclusion is actually defined for the closed neighborhood (\(N[j]=N(j) \cup \{j\}\)). This is due to some
mathematical peculiarities when \(i\) and \(j\) are connected. Neighborhood-inclusion defines a
partial ranking of the nodes. That is, some node pairs will not be comparable, because neither
\(N(i) \subseteq N[j]\) nor \(N(j) \subseteq N[i]\) will hold. If the neighborhood of a node \(i\)
is properly contained in the neighborhood of \(j\), then we will say that \(i\) is *dominated* by \(j\).

We can calculate all pairs of neighborhood-inclusion with the function `neighborhood_inclusion()`

in the `netrankr`

package.

```
P1 <- neighborhood_inclusion(g1)
P2 <- neighborhood_inclusion(g2)
```

An entry \(P[i,j]\) is one if \(N(i)\subseteq N[j]\) and zero otherwise. With the function
`comparable_pairs()`

, we can check the fraction of comparable pairs. Let us start with the first network.

`comparable_pairs(P1)`

`## [1] 0.1636364`

Only 16% of pairs are comparable with neighborhood-inclusion. For a better understanding of the dominance relations, we can also visualize them as a graph.

`d1 <- dominance_graph(P1)`

An edge \((i,j)\) is present, if \(P[i,j]=1\) and thus \(i\) is dominated by \(j\).
Centrality indices will always put these comparable pairs in the same order.
To check this, we use the `all_indices()`

function from the last post again.

`res <- all_indices(g1)`

Let us focus on the triple \(1,3,5\).

`P1[c(1,3,5),c(1,3,5)] #(compare also with the dominance graph)`

```
## [,1] [,2] [,3]
## [1,] 0 1 1
## [2,] 0 0 1
## [3,] 0 0 0
```

So, indices should rank them as \(1\leq3\leq 5\).

```
ranks135 <- apply(res[c(1,3,5),],2,rank)
rownames(ranks135) <- c(1,3,5)
ranks135
```

```
## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13]
## 1 1 1.5 1 1 1.5 1 1 1.5 1 1 1.5 1 1
## 3 2 1.5 2 2 1.5 2 2 1.5 2 2 1.5 2 2
## 5 3 3.0 3 3 3.0 3 3 3.0 3 3 3.0 3 3
## [,14] [,15] [,16] [,17] [,18] [,19] [,20] [,21] [,22] [,23] [,24] [,25]
## 1 1 1 1 1 1 1 1 2 1.5 1 1 1
## 3 2 2 2 2 2 2 2 2 1.5 2 2 2
## 5 3 3 3 3 3 3 3 2 3.0 3 3 3
## [,26] [,27] [,28] [,29] [,30] [,31] [,32] [,33] [,34] [,35]
## 1 1 1 1 1.0 1 1 1 1 1 1
## 3 2 2 2 2.5 2 2 2 2 2 2
## 5 3 3 3 2.5 3 3 3 3 3 3
```

All 35 indices indeed produce a ranking that is in accordance with what we postulated. (Ties are allowed in the ranking since we require “\(\leq\)” and not “\(<\)” ).

The `is_preserved()`

function can be used to check if all dominance relations are
preserved in the index induced rankings.

`apply(res,2, function(x) is_preserved(P1,x))`

```
## [1] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [15] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [29] TRUE TRUE TRUE TRUE TRUE TRUE TRUE
```

For the other 84% of pairs that are not comparable by neighborhood-inclusion, indices are “at liberty” to rank nodes differently. Take the triple \(6,7,8\) as an example.

`P1[6:8,6:8] #(compare also with the dominance graph)`

```
## [,1] [,2] [,3]
## [1,] 0 0 0
## [2,] 0 0 0
## [3,] 0 0 0
```

```
ranks678 <- apply(res[6:8,],2,rank)
rownames(ranks678) <- 6:8
# unique rankings of 6,7,8
ranks678[,!duplicated(t(ranks678))]
```

```
## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8]
## 6 2 2 3 2 2.5 2.5 1.5 3
## 7 2 1 1 3 1.0 2.5 1.5 2
## 8 2 3 2 1 2.5 1.0 3.0 1
```

The 35 indices produce 8 distinct rankings of \(6,7,8\). This means that whenever a pair of nodes \(i\) and \(j\) are not comparable with neighborhood-inclusion, it is (theoretically) possible to construct an index for each of the three possible rankings (\(i<j\), \(j<i\), \(i\sim j\))

Moving on to the second network.

`comparable_pairs(P2)`

`## [1] 1`

So all pairs are comparable by neighborhood-inclusion. Hence, all indices will induce the same ranking (up to some potential tied ranks, but no discordant pairs), as we already observed in the previous post.

# Threshold graphs and correlation among indices

The second example network is part of the class of *threshold graphs*. One of their
defining features is that the partial ranking induced by neighborhood-inclusion is in fact
a ranking. A random threshold graph can be created with the `threshold_graph()`

function.
The function takes two parameters, one for the number of nodes, and one (approximately) for
the density. The class includes some well known graphs, such as the two below.

```
tg1 <- threshold_graph(n=10,p=1)
tg2 <- threshold_graph(n=10,p=0)
```

We know from the previous section that centrality indices will always produce the same ranking on these graphs. This allows us to reason about another topic that is frequently investigated: correlations among indices. Correlations are often attributed to the definitions of indices. Take closeness and betweenness. On first glance, they measure very different things: Being close to all nodes and being “in between” all nodes. Hence, we would expect them to be only weakly correlated. But threshold graphs give us a reason to believe, that correlations are not entirely dependent on the definitions but rather on structural features of the network. (This article gives more details and references on that topic. Let me know if you can’t access it).

As an illustration, we compare betweenness and closeness on a threshold graph and a threshold graph with added noise from a random graph.

```
#threshold graph
tg3 <- threshold_graph(100,0.2)
#noise graph
gnp <- sample_gnp(100,0.01)
A1 <- get.adjacency(tg3,sparse=F)
A2 <- get.adjacency(gnp,sparse=F)
#construct a noise threshold graph
tg3_noise <- graph_from_adjacency_matrix(xor(A1,A2))
#calculate discordant pairs for betweenness and closeness in both networks
disc1 <- compare_ranks(betweenness(tg3),closeness(tg3))$discordant
disc2 <- compare_ranks(betweenness(tg3_noise),closeness(tg3_noise))$discordant
c(disc1,disc2)
```

`## [1] 0 719`

On the threshold graph we do not observe any discordant pairs for the two indices. However, the little noise we added to the threshold graph was enough to introduce 719 pairs of nodes that are now ranked differently. In general, we can say that

*The closer a network is to be a threshold graph, the higher we expect the correlation of any pair of centrality indices to be, independent of their definition.*

But how to define *being close* to a threshold graph? One obvious choice is to
use the function `comparable_pairs()`

. The more pairs are comparable, the less possibilities
for indices to rank the nodes differently. Hence, we are close to a unique ranking obtained
for threshold graphs. A second option is to use an appropriate distance measure for graphs.
`netrankr`

implements the so called *majorization gap* which operates on the degree sequences of graphs.
In its essence, it returns the number of edges that need to be rewired, in order to turn an
arbitrary graph into a threshold graph.

```
mg1 <- majorization_gap(tg3)
mg2 <- majorization_gap(tg3_noise)
c(mg1,mg2)
```

`## [1] 0.0000000 0.1235452`

The result is given as a fraction of the total number of edges. So 12%
of edges need to be rewired in the noisy graph to turn it into a threshold graph.
To get the raw count, set `norm=FALSE`

.

`majorization_gap(tg3_noise,norm = FALSE)`

`## [1] 276`

# Summary

Neighborhood-inclusion seems to be a property that underlies many centrality indices.
If a node \(i\) is dominated by another node \(j\), then (almost) any index will rank \(j\)
higher than \(i\). I am not going to make the bold statement of saying that **all** centrality indices
have this property, although all commonly used and traditional indices have this property. However, it is easy to come up with an index that doesn’t preserve the partial ranking (Coincidentally, the two hyperbolic indices from the first post don’t preserve it. Thank god!). But if we accept the preservation of neighborhood-inclusion to be a defining property of
centrality indices, then we are able to a) derive more theoretical results about centrality (see correlation section)
b) distinguish proper indices from invalid ones (see hyperbolic indices)
and c) think about new ways of assessing centrality, that do not necessarily rely on indices.

Point c) will be partially addressed in the next post and in more detail in subsequent ones.

The main focus for the next post is on how to extend neighborhood-inclusion to other forms of dominance.
Additionally, we will see how to deconstruct indices into a series of building blocks,
which allows for a deeper understanding on what indices actually “measure”.