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Link to original content: https://doi.org/10.4204/EPTCS.264.2
[1802.01788v1] Aggregate Graph Statistics
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Computer Science > Distributed, Parallel, and Cluster Computing

arXiv:1802.01788v1 (cs)
[Submitted on 6 Feb 2018]

Title:Aggregate Graph Statistics

Authors:Giorgio Audrito (1), Ferruccio Damiani (1), Mirko Viroli (2) ((1) University of Torino, (2) University of Bologna)
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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.
Comments: In Proceedings ALP4IoT 2017, arXiv:1802.00976
Subjects: Distributed, Parallel, and Cluster Computing (cs.DC)
ACM classes: C.2.4;D.1.3;D.3.2
Cite as: arXiv:1802.01788 [cs.DC]
  (or arXiv:1802.01788v1 [cs.DC] for this version)
  https://doi.org/10.48550/arXiv.1802.01788
Journal reference: EPTCS 264, 2018, pp. 18-22
Related DOI: https://doi.org/10.4204/EPTCS.264.2

Submission history

From: EPTCS [view email] [via EPTCS proxy]
[v1] Tue, 6 Feb 2018 04:09:51 UTC (14 KB)
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