# bonpow

##### Find Bonacich Power Centrality Scores of Network Positions

`bonpow`

takes a graph (`dat`

) and returns the
Boncich power centralities of positions (selected by `nodes`

).
The decay rate for power contributions is specified by `exponent`

(1 by default).

- Keywords
- graphs

##### Usage

```
bonpow(graph, nodes=V(graph), loops=FALSE, exponent=1,
rescale=FALSE, tol=1e-7)
```

##### Arguments

- graph
- the input graph.
- nodes
- vertex sequence indicating which vertices are to be included in the calculation. By default, all vertices are included.
- loops
- boolean indicating whether or not the diagonal should be
treated as valid data. Set this true if and only if the data can
contain loops.
`loops`

is`FALSE`

by default. - exponent
- exponent (decay rate) for the Bonacich power centrality score; can be negative
- rescale
- if true, centrality scores are rescaled such that they sum to 1.
- tol
- tolerance for near-singularities during matrix inversion
(see
`solve`

)

##### Details

Bonacich's power centrality measure is defined by
$C_{BP}\left(\alpha,\beta\right)=\alpha\left(\mathbf{I}-\beta\mathbf{A}\right)^{-1}\mathbf{A}\mathbf{1}$, where $\beta$ is an attenuation parameter
(set here by `exponent`

) and $\mathbf{A}$ is the graph
adjacency matrix. (The coefficient $\alpha$ acts as a
scaling parameter, and is set here (following Bonacich (1987)) such that
the sum of squared scores is equal to the number of vertices. This
allows 1 to be used as a reference value for the ``middle'' of the
centrality range.) When $\beta \rightarrow
1/\lambda_{\mathbf{A}1}$ (the reciprocal of the
largest eigenvalue of $\mathbf{A}$), this is to within a constant
multiple of the familiar eigenvector centrality score; for other values
of $\beta$, the behavior of the measure is quite different. In
particular, $\beta$ gives positive and negative weight to even and
odd walks, respectively, as can be seen from the series expansion
$C_{BP}\left(\alpha,\beta\right)=\alpha \sum_{k=0}^\infty \beta^k
\mathbf{A}^{k+1} \mathbf{1}$ which converges so long as $|\beta|
< 1/\lambda_{\mathbf{A}1}$. The magnitude of
$\beta$ controls the influence of distant actors on ego's
centrality score, with larger magnitudes indicating slower rates of
decay. (High rates, hence, imply a greater sensitivity to edge
effects.)

Interpretively, the Bonacich power measure corresponds to the notion
that the power of a vertex is recursively defined by the sum of the
power of its alters. The nature of the recursion involved is then
controlled by the power exponent: positive values imply that vertices
become more powerful as their alters become more powerful (as occurs in
cooperative relations), while negative values imply that vertices become
more powerful only as their alters become *weaker* (as occurs in
competitive or antagonistic relations). The magnitude of the exponent
indicates the tendency of the effect to decay across long walks; higher
magnitudes imply slower decay. One interesting feature of this measure
is its relative instability to changes in exponent magnitude
(particularly in the negative case). If your theory motivates use of
this measure, you should be very careful to choose a decay parameter on
a non-ad hoc basis.

##### Value

- A vector, containing the centrality scores.

##### Note

This function was ported (ie. copied) from the SNA package.

##### concept

Bonacich Power centrality

##### Warning

Singular adjacency matrices cause no end of headaches
for this algorithm; thus, the routine may fail in certain cases. This
will be fixed when I get a better algorithm. `bonpow`

will not
symmetrize your data before extracting eigenvectors; don't send this
routine asymmetric matrices unless you really mean to do so.

##### References

Bonacich, P. (1972). ``Factoring and Weighting Approaches
to Status Scores and Clique Identification.'' *Journal of
Mathematical Sociology*, 2, 113-120.

Bonacich, P. (1987). ``Power and Centrality: A Family of Measures.''
*American Journal of Sociology*, 92, 1170-1182.

##### See Also

##### Examples

```
# Generate some test data from Bonacich, 1987:
g.c <- graph( c(1,2,1,3,2,4,3,5)-1, dir=FALSE)
g.d <- graph( c(1,2,1,3,1,4,2,5,3,6,4,7)-1, dir=FALSE)
g.e <- graph( c(1,2,1,3,1,4,2,5,2,6,3,7,3,8,4,9,4,10)-1, dir=FALSE)
g.f <- graph( c(1,2,1,3,1,4,2,5,2,6,2,7,3,8,3,9,3,10,4,11,4,12,4,13)-1, dir=FALSE)
# Compute Bonpow scores
for (e in seq(-0.5,.5, by=0.1)) {
print(round(bonpow(g.c, exp=e)[c(1,2,4)], 2))
}
for (e in seq(-0.4,.4, by=0.1)) {
print(round(bonpow(g.d, exp=e)[c(1,2,5)], 2))
}
for (e in seq(-0.4,.4, by=0.1)) {
print(round(bonpow(g.e, exp=e)[c(1,2,5)], 2))
}
for (e in seq(-0.4,.4, by=0.1)) {
print(round(bonpow(g.f, exp=e)[c(1,2,5)], 2))
}
```

*Documentation reproduced from package igraph, version 0.5.1, License: GPL (>= 2)*