Epidemic diffusion of social updates in Dunbar-based DOSN (Contributo in atti di convegno)

Type
Label
  • Epidemic diffusion of social updates in Dunbar-based DOSN (Contributo in atti di convegno) (literal)
Anno
  • 2014-01-01T00:00:00+01:00 (literal)
Alternative label
  • Conti M.; De Salve A.; Guidi B.; Guidi B.; Ricci L. (2014)
    Epidemic diffusion of social updates in Dunbar-based DOSN
    in Euro-Par 2014 International Workshops, Porto, Portugal
    (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
  • Conti M.; De Salve A.; Guidi B.; Guidi B.; Ricci L. (literal)
Pagina inizio
  • 311 (literal)
Pagina fine
  • 322 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#url
  • http://www.scopus.com/inward/record.url?eid=2-s2.0-84917705692&partnerID=q2rCbXpz (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroVolume
  • 8805 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#volumeInCollana
  • 8805 (literal)
Rivista
Note
  • Scopu (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
  • University of Pisa - Department of Computer Science, Largo B. Pontecorvo, Pisa, 56127, Italy; IIT-CNR, via G. Moruzzi, Pisa, 1 56124, Italy (literal)
Titolo
  • Epidemic diffusion of social updates in Dunbar-based DOSN (literal)
Abstract
  • Distributed Online Social Networks (DOSNs) do not rely on a central repository for storing social data so that the users can keep control of their private data and do not depend on the social network provider. The ego network, i.e. the network made up of an individual, the ego, along with all the social ties she has with other people, the alters, may be exploited to define distributed social overlays and dissemination protocols. In this paper we propose a new epidemic protocol able to spread social updates in Dunbar-based DOSN overlays where the links between nodes are defined by considering the social interactions between users. Our approach is based on the notion of Weighted Ego Betweenness Centrality (WEBC) which is an egocentric social measure approximating the Betweenness Centrality. The computation of the WEBC exploits a weighted graph where the weights correspond to the tie strengths between the users so that nodes having a higher number of interactions are characterized by a higher value of the WEBC. A set of experimental results proving the effectiveness of our approach is presented. (literal)
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