  In network science, there is often the need to sort the graph nodes. While
the sorting strategy may be different, in general sorting is performed by
exploiting the network structure. In particular, the metric PageRank has been
used in the past decade in different ways to produce a ranking based on how
many neighbors point to a specific node. PageRank is simple, easy to compute
and effective in many applications, however it comes with a price: as PageRank
is an application of the random walker, the arc weights need to be normalized.
This normalization, while necessary, introduces a series of unwanted
side-effects. In this paper, we propose a generalization of PageRank named
Black Hole Metric which mitigates the problem. We devise a scenario in which
the side-effects are particularily impactful on the ranking, test the new
metric in both real and synthetic networks, and show the results.
