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arXiv · 1802.01788

Aggregate Graph Statistics

Giorgio Audrito·Ferruccio Damiani·Mirko Viroli
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Abstract

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.

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Giorgio Audrito, Ferruccio Damiani, Mirko Viroli. 2018-02-06. Aggregate Graph Statistics. https://doi.org/10.4204/eptcs.264.2

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