Studying network dynamics in digital ecosystems (Contributo in atti di convegno)

Type
Label
  • Studying network dynamics in digital ecosystems (Contributo in atti di convegno) (literal)
Anno
  • 2009-01-01T00:00:00+01:00 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi
  • 10.1145/1643823.1643829 (literal)
Alternative label
  • Caschera Maria Chiara, D'Ulizia Arianna, Ferri Fernando, Grifoni Patrizia (2009)
    Studying network dynamics in digital ecosystems
    in MEDES '09: Proceedings of the International conference on Management of Emergent Digital EcoSystems, Lyon (FR), 27 October 2009-30 October 2009
    (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
  • Caschera Maria Chiara, D'Ulizia Arianna, Ferri Fernando, Grifoni Patrizia (literal)
Pagina inizio
  • 21 (literal)
Pagina fine
  • 27 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#titoloVolume
  • Proceedings of the International Conference on Management of Emergent Digital EcoSystems, MEDES '09 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#note
  • Data evento : 27-30 October 2009. ACM:New York, USA; ISBN: 978-1-60558-829-2 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#descrizioneSinteticaDelProdotto
  • One of the main fields in which the research on Digital Ecosystems has been fruitfully applied is the networking field, with the aim of discovering the dynamics of relationships among the entities of the ecosystems. Following this research direction, this paper addresses the problem of predicting social dynamics of a network in order to emphasize the relationships and the potentials for collaboration and transmission of knowledge, as well as the nature and intensity of the inner sub-networks. To do this, a multi-layer Hidden Markov Model has been applied, which allows predicting the evolution of the interests and intensity of the overall network, based on the most probable evolution of each sub-network (e.g., if it increases, decreases, appears, etc.). This model has been tested using data from a large, realistic network and the prediction accuracy rate has been evaluated. (literal)
Note
  • Scopu (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
  • Caschera Maria Chiara, D'Ulizia Arianna,Ferri Fernando, Grifoni Patrizia : IRPPS-CNR (literal)
Titolo
  • Studying network dynamics in digital ecosystems (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#isbn
  • 978-1-60558-829-2 (literal)
Abstract
  • One of the main fields in which the research on Digital Ecosystems has been fruitfully applied is the networking field, with the aim of discovering the dynamics of relationships among the entities of the ecosystems. Following this research direction, this paper addresses the problem of predicting social dynamics of a network in order to emphasize the relationships and the potentials for collaboration and transmission of knowledge, as well as the nature and intensity of the inner sub-networks. To do this, a multi-layer Hidden Markov Model has been applied, which allows predicting the evolution of the interests and intensity of the overall network, based on the most probable evolution of each sub-network (e.g., if it increases, decreases, appears, etc.). This model has been tested using data from a large, realistic network and the prediction accuracy rate has been evaluated. (literal)
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