Scale-free and power law distributions via fixed points and convergence of (thinning and conditioning) transformations

In discrete contexts such as the degree distribution for a graph, \emph{scale-free} has traditionally been \emph{defined} to be \emph{power-law}. We propose a reasonable interpretation of \emph{scale-free}, namely, invariance under the transformation of $p$-thinning, followed by conditioning on being positive. For each $\beta \in (1,2)$, we show that there is a unique distribution which is a fixed point of this transformation; the distribution is power-law-$\beta$, and different from the usual Yule–Simon power law-$\beta$ that arises in preferential attachment models. In addition to characterizing these fixed points, we prove convergence results for iterates of the transformation.