Measuring the driving force in evolving networks
These functions assume a simple evolving network model and measure the functional form of a so-called attractiveness function governing the evolution of the network.
measure.dynamics.idage(graph, agebins=300, iterations=5, sd=FALSE, estind=NULL, estage=NULL)
- The graph of which the evolution is quantified. It is assumed that the vertices were added in increasing order of vertex id.
- Numeric constant, the number of bins to use for measuring aging.
- Numeric constant, number of iterations to perform while calculating the attractiveness and the total attractiveness function.
- Logical, should an estimation of the error also calculated.
The functions should be considered as experimental, so no detailed documentation yet. Sorry.
- A named list with components
akl(the attractiveness function),
st(total attractiveness versus time),
sd(only if the
TRUE, the estimation of the error for the
g <- barabasi.game(10000) mes <- measure.dynamics.idage(g, 20, sd=TRUE)