The value of a is used to normalize the measure, the value of b is an attenuation factor which gives the amount of dependence of each vertex's centrality on the centralities of the vertices it is adjacent to. The normalization parameter is automatically selected so that the square root of the sum of squares of the vertex centralities is the size of the network. (That is the Euclidean norm of the vector equals the number of vertices).

The parameter b is selected by the user, negative values should be selected if an individual's power is increased by being connected to vertices with low power and positive values selected if an individual's power is increased by being connected to vertices with high power. Note a value of zero would give the out-degree of each vertex.

The routine calculates power centrality and some descriptive statistics of the measure. To methods are given one method is exact but this can be slow for large networks. The other is an iterative routine which will give the same normalized answer if allowed to run for a long time in all but a few unusual situations.

Name of file which contains power centrality measure for every vertex. The extension depends on the method chosen.

Iterative gives a power method calculation. This can sometimes not converge regardless of the walk length, although this is rare.

A value of 0 gives a centrality measure directly proportional to the out-degree of each vertex. Positive values give weight to being connected to powerful actors, negative values give weight to being connected to low powered actors. Larger values in modulus gives greater weight to actors further away. This parameter must be smaller in modulus than the reciprocal of the largest eigenvalue. To find the highest positive value then clicking on the