Aggregate Graph Statistics
Giorgio Audrito, Ferruccio Damiani, Mirko Viroli
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.
Danilo Pianini, Guido Salvaneschi (eds.)
@inproceedings{ADV-ALP4IOT2018,
author = {Audrito, Giorgio and Damiani, Ferruccio and Viroli, Mirko},
title = {Aggregate Graph Statistics},
booktitle = {Proceedings First Workshop on Architectures, Languages and Paradigms
for IoT, ALP4IoT at iFM 2017, Turin, Italy, September 18, 2017.},
pages = {18--22},
year = {2017},
url = {https://doi.org/10.4204/EPTCS.264.2},
doi = {10.4204/EPTCS.264.2},
editor = {Pianini, Danilo and Salvaneschi, Guido},
series = {EPTCS},
volume = {264},
year = {2018}
}