Aggregate Graph Statistics

  title={Aggregate Graph Statistics},
  author={Giorgio Audrito and Ferruccio Damiani and 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… CONTINUE READING
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Building Blocks for Aggregate Programming of Self-Organising Applications

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