Aggregate Graph Statistics

Giorgio Audrito
(University of Torino)
Ferruccio Damiani
(University of Torino)
Mirko Viroli
(University of Bologna)

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.

In Danilo Pianini and Guido Salvaneschi: Proceedings First Workshop on Architectures, Languages and Paradigms for IoT (ALP4IoT 2017), Turin, Italy, September 18, 2017, Electronic Proceedings in Theoretical Computer Science 264, pp. 18–22.
Published: 3rd February 2018.

ArXived at: http://dx.doi.org/10.4204/EPTCS.264.2 bibtex PDF
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