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.

Electronic Proceedings in Theoretical Computer Science, EPTCS 264, pages 18--22, 5 pages, 2018.
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}
}