This vignette describes methods to analyse all possible centrality rankings of a network at once. To do so, a partial rankings as computed from neighborhood-inclusion or, more general, positional dominance is needed. In this vignette we focus on neighborhood-inclusion but note that all considered methods are readily applicable for positional dominance. For more examples consult the tutorial.

Neighborhood-inclusion or induces a partial ranking on the vertices of a graph $G=(V,E)$.
We write $u\leq v$ if $N(u)\subseteq N[v]$ holds for two vertices $u,v \in V$.
From the fact that
$$
u\leq v \implies c(u) \leq c(v)
$$
holds for any centrality index $c:V\to \mathbb{R}$, we can characterize the set of all *possible*
centrality based node rankings. Namely as the set of rankings that extend the partial ranking
"$\leq$" to a (complete) ranking.

\
A node ranking can be defined as a mapping
$$rk: V \to {1,\ldots,n},$$
where we use the convention that $u$ is the top ranked node if $rk(u)=n$ and the
bottom ranked one if $rk(u)=1$. The set of all possible rankings can then be characterized as
$$
\mathcal{R}(\leq)={rk:V \to {1,\ldots,n}\; : \; u\leq v \implies rk(u)\leq rk(v)}.
$$
This set contains **all** rankings that could be obtained with a centrality index.

\
Once $\mathcal{R}(\leq)$ is calculated, it can be used for a probabilistic assessment of centrality,
analyzing all possible rankings at once. Examples include *relative rank probabilities*
(How likely is it, that a node $u$ is more central than another node $v$?) or
*expected ranks* (How central do we expect a node $u$ to be).

\
It most be noted though, that deriving the set $\mathcal{R}(\leq)$ quickly becomes
infeasible for larger networks, and one has to resort to approximation methods.
These and more theoretical details can be found in

Schoch, David. (2018). Centrality without Indices: Partial rankings and rank Probabilities in networks.

Social Networks,54, 50-60.(link)

knitr::opts_chunk$set(fig.width=5,fig.align = 'center')

`netrankr`

Package```
library(Matrix)
```

library(netrankr) library(igraph) library(magrittr)

Before calculating any probabilities consider the following example graph and the rankings induced by various centrality indices, shown as rank intervals (consult this vignette for details).

data("dbces11") g <- dbces11 #neighborhood inclusion P <- g %>% neighborhood_inclusion(sparse = FALSE) #without %>% operator: # P <- neighborhood_inclusion(g) cent_scores <- data.frame( degree=degree(g), betweenness=round(betweenness(g),4), closeness=round(closeness(g),4), eigenvector=round(eigen_centrality(g)$vector,4), subgraph=round(subgraph_centrality(g),4)) plot(rank_intervals(P),cent_scores = cent_scores)

Notice how all five indices rank a different vertex as the most central one.

\
In the following subsections the output of the function `exact_rank_prob()`

are described which may help to circumvent the potential arbitrariness of index induced rankings.
But first, let us briefly look at all the return values.

res <- exact_rank_prob(P) res

The function returns an object of type \emph{netrankr_full} which contains the result of a full probabilistic rank analysis. The specific list entries are discussed in the following subsections.

Instead of insisting on fixed ranks of nodes as given by indices, we can use *rank probabilities*
to assess the likelihood of certain rank. Formally, rank probabilities are simply defined as
$$
P(rk(u)=k)=\frac{\lvert {rk \in \mathcal{R}(\leq) \; : \; rk(u)=k} \rvert}{\lvert \mathcal{R}(\leq) \rvert}.
$$
Rank probabilities are given by the return value `rank.prob`

of the `exact_rank_prob()`

function.

rp <- round(res$rank.prob,2) rp

Entries `rp[u,k]`

correspond to $P(rk(u)=k)$.

\
The most interesting probabilities are certainly $P(rk(u)=n)$, that is how likely
is it for a node to be the most central.

```
rp[,11]
```

Recall from the previous section that we found five indices that ranked $6,7,8,10$ and $11$ on top. The probability tell us now, how likely it is to find an index that rank these nodes on top. In this case, node $11$ has the highest probability to be the most central node.

In some cases, we might not necessarily be interested in a complete ranking of nodes,
but only in the relative position of a subset of nodes. This idea leads to
*relative rank probabilities*, that is formally defined as
$$
P(rk(u)\leq rk(v))=\frac{\lvert {rk \in \mathcal{R}(\leq) \; : \; rk(u)\leq rk(v)} \rvert}{\lvert \mathcal{R}(\leq) \rvert}.
$$
Relative rank probabilities are given by the return value `relative.rank`

of the `exact_rank_prob()`

function.

rrp <- round(res$relative.rank,2) rrp

Entries `rrp[u,v]`

correspond to $P(rk(u)\leq rk(v))$.

\
The more a value `rrp[u,v]`

deviates from $0.5$ towards $1$, the more confidence we gain
that a node $v$ is more central than a node $u$.

The *expected rank* of a node in centrality rankings is defined as the expected
value of the rank probability distribution. That is,
$$
\rho(u)=\sum_{k=1}^n k\cdot P(rk(u)=k).
$$
Expected ranks are given by the return value `expected.rank`

of the `exact_rank_prob()`

function.

ex_rk <- round(res$expected.rank,2) ex_rk

As a reminder, the higher the numeric rank, the more central a node is. In this case, node $11$ has the highest expected rank in any centrality ranking.

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