FLIP-CPM: A Parallel Community Detection Method (Contributo in atti di convegno)

Type
Label
  • FLIP-CPM: A Parallel Community Detection Method (Contributo in atti di convegno) (literal)
Anno
  • 2012-01-01T00:00:00+01:00 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi
  • 10.1007/978-1-4471-2155-8_31 (literal)
Alternative label
  • Gregori Enrico, Mainardi Simone, Orsini Chiara, Lenzini Luciano (2012)
    FLIP-CPM: A Parallel Community Detection Method
    in 26th International Symposium on Computer and Information Sciences (ISCIS 2011), London (UK), 2011
    (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
  • Gregori Enrico, Mainardi Simone, Orsini Chiara, Lenzini Luciano (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#altreInformazioni
  • ID_PUMA: cnr.iit/2011-A2-064. Area di valutazione 15a - Scienze e tecnologie per una società dell'informazione e della comunicazione. ID Mod. Commessa INT.P01.001.002 - 044 - Ubiquitous Internet. Articolo in Atti di convegno internazionale con referee. (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#url
  • http://dx.doi.org/10.1007/978-1-4471-2155-8_31 (literal)
Note
  • Scopu (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
  • CNR-IIT, Pisa, Italy; CNR-IIT, Pisa, Italy;CNR-IIT, Pisa, Italy; Dept. of Information Engineering University of Pisa, Italy (literal)
Titolo
  • FLIP-CPM: A Parallel Community Detection Method (literal)
Abstract
  • Uncovering the underlying community structure of the Internet at the AS level is essential way to gain insight both into its structure and its functional organization. Of all the de?nitions of community proposed by researchers, we focused on the k-clique community de?nition as we believe it best catches the characteristics of the Internet AS-level topology. Extracting k-clique communities using the methods available in the literature requires a formidable amount of computational load and memory resources. In this paper we propose a new parallel method that has proved its capability in extracting communities e?ciently and e?ectively from realworld complex networks, including the Internet at the AS level. This innovative method is much less resource intensive than Clique Percolation Method and experimental results show it is always at least an order of magnitude faster. In addition, tests run on parallel architectures show a noticeable speedup factor, in some cases linear with the number of cores. (literal)
Prodotto di
Autore CNR
Insieme di parole chiave

Incoming links:


Prodotto
Autore CNR di
Insieme di parole chiave di
data.CNR.it