Aggregate Graph Statistics - Giorgio Audrito (Electronic Proceedings in Theoretical Computer Science, 2018)
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.