  Collecting statistic from graph-based data is an increasingly studied topic
in the data mining community. We argue that these statistics have great value
as well in dynamic IoT contexts: they can support complex computational
activities involving distributed coordination and provision of situation
recognition. We show that the HyperANF algorithm for calculating the
neighbourhood function of vertices of a graph naturally allows for a fully
distributed and asynchronous implementation, thanks to a mapping to the field
calculus, a distribution model proposed for collective adaptive systems. This
mapping gives evidence that the field calculus framework is well-suited to
accommodate massively parallel computations over graphs. Furthermore, it
provides a new "self-stabilising" building block which can be used in aggregate
computing in several contexts, there including improved leader election or
network vulnerabilities detection.
