Evolving networks: eras and turning points (Articolo in rivista)

Type

• Prodotto della ricerca (Classe)
• Articolo in rivista (Classe)

Label

• Evolving networks: eras and turning points (Articolo in rivista) (literal)

Anno

• 2013-01-01T00:00:00+01:00 (literal)

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

• 10.3233/IDA-120566 (literal)

Alternative label

• Berlingerio M., Coscia M., Giannotti F., Monreale A., Pedreschi D. (2013)
  Evolving networks: eras and turning points
  in Intelligent data analysis
  (literal)

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

• Berlingerio M., Coscia M., Giannotti F., Monreale A., Pedreschi D. (literal)

Pagina inizio

• 27 (literal)

Pagina fine

• 48 (literal)

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

• 17 (literal)

Rivista

• Intelligent data analysis (Rivista)

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

• 1 (literal)

Note

• ISI Web of Science (WOS) (literal)
• PuMa (literal)
• Scopu (literal)

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

• CNR-ISTI, Pisa, Italy; CNR-ISTI, Pisa, Italy; CNR-ISTI, Pisa, italy; Department of Computer Science, University of Pisa, Pisa, Italy; Department of Computer Science, University of Pisa, Italy (literal)

Titolo

• Evolving networks: eras and turning points (literal)

Abstract

• Within the large body of research in complex network analysis, an important topic is the temporal evolution of networks. Existing approaches aim at analyzing the evolution on the global and the local scale, extracting properties of either the entire network or local patterns. In this paper, we focus on detecting clusters of temporal snapshots of a network, to be interpreted as eras of evolution. To this aim, we introduce a novel hierarchical clustering methodology, based on a dissimilarity measure (derived from the Jaccard coefficient) between two temporal snapshots of the network, able to detect the turning points at the beginning of the eras. We devise a framework to discover and browse the eras, either in top-down or a bottom-up fashion, supporting the exploration of the evolution at any level of temporal resolution. We show how our approach applies to real networks and null models, by detecting eras in an evolving co-authorship graph extracted from a bibliographic dataset, a collaboration graph extracted from a cinema database, and a network extracted from a database of terrorist attacks; we illustrate how the discovered temporal clustering highlights the crucial moments when the networks witnessed profound changes in their structure. Our approach is finally boosted by introducing a meaningful labeling of the obtained clusters, such as the characterizing topics of each discovered era, thus adding a semantic dimension to our analysis. (literal)

Prodotto di

• Knowledge Discovery and Data Mining (ICT.P08.009.003) (Modulo)
• Istituto di scienza e tecnologie dell'informazione \"Alessandro Faedo\" (ISTI) (Istituto)

Autore CNR

• Michele Berlingerio (Persona)
• Michele Coscia (Persona)
• FOSCA GIANNOTTI (Unità di personale interno)

Insieme di parole chiave

• Keywords of "Evolving networks: eras and turning points" (Insieme di parole chiave)

Incoming links:

Prodotto

• Istituto di scienza e tecnologie dell'informazione \"Alessandro Faedo\" (ISTI) (Istituto)
• Knowledge Discovery and Data Mining (ICT.P08.009.003) (Modulo)

Autore CNR di

• FOSCA GIANNOTTI (Unità di personale interno)
• Michele Coscia (Persona)
• Michele Berlingerio (Persona)

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

• Intelligent data analysis (Rivista)

Insieme di parole chiave di

• Keywords of "Evolving networks: eras and turning points" (Insieme di parole chiave)