A Multiobjective and Evolutionary Clustering Method for Dynamic Networks (Contributo in atti di convegno)

Type

• Prodotto della ricerca (Classe)
• Contributo in atti di convegno (Classe)

Label

• A Multiobjective and Evolutionary Clustering Method for Dynamic Networks (Contributo in atti di convegno) (literal)

Anno

• 2010-01-01T00:00:00+01:00 (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi

• 10.1109/ASONAM.2010.23 (literal)

Alternative label

• Francesco Paolo Folino; Clara Pizzuti (2010)
  A Multiobjective and Evolutionary Clustering Method for Dynamic Networks
  in International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2010), Odense, Danimarca, 9-11 Agosto 2010
  (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori

• Francesco Paolo Folino; Clara Pizzuti (literal)

Pagina inizio

• 256 (literal)

Pagina fine

• 263 (literal)

Note

• Google Scholar (literal)
• DBLP (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni

• Istituto di calcolo e reti ad alte prestazioni Istituto di calcolo e reti ad alte prestazioni (literal)

Titolo

• A Multiobjective and Evolutionary Clustering Method for Dynamic Networks (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#isbn

• 978-0-7695-4138-9 (literal)

Abstract

• The discovery of evolving communities in dynamic networks is an important research topic that poses challenging tasks. Previous evolutionary based clustering methods try to maximize cluster accuracy, with respect to incoming data of the current time step, and minimize clustering drift from one time step to the successive one. In order to optimize both these two competing objectives, an input parameter that controls the preference degree of a user towards either the snapshot quality or the temporal quality is needed. In this paper the detection of communities with temporal smoothness is formulated as a multiobjective problem and a method based on genetic algorithms is proposed. The main advantage of the algorithm is that it automatically provides a solution representing the best trade-off between the accuracy of the clustering obtained, and the deviation from one time step to the successive. Experiments on synthetic data sets show the very good performance of the method compared to state-of-the-art approaches. (literal)

Prodotto di

• Gestire, Estrarre Conoscenza e Ragionare sui Dati (ICT.P08.002.001) (Modulo)
• Institute for high performance computing and networking (ICAR) (Istituto)

Autore CNR

• CLARA PIZZUTI (Unità di personale interno)
• FRANCESCO PAOLO FOLINO (Unità di personale interno)

Incoming links:

Prodotto

• Institute for high performance computing and networking (ICAR) (Istituto)
• Gestire, Estrarre Conoscenza e Ragionare sui Dati (ICT.P08.002.001) (Modulo)

Autore CNR di

• CLARA PIZZUTI (Unità di personale interno)
• FRANCESCO PAOLO FOLINO (Unità di personale interno)