A classification for community discovery methods in complex networks (Rapporti tecnici/preprint/working paper)

Type

• Prodotto della ricerca (Classe)
• Rapporti tecnici/preprint/working paper (Classe)

Label

• A classification for community discovery methods in complex networks (Rapporti tecnici/preprint/working paper) (literal)

Anno

• 2012-01-01T00:00:00+01:00 (literal)

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

• 10.1002/sam.10133 (literal)

Alternative label

• Coscia M., Giannotti F., Pedreschi D. (2012)
  A classification for community discovery methods in complex networks
  
  (literal)

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

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

Pagina inizio

• 512 (literal)

Pagina fine

• 546 (literal)

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

• Lavoro archiviato in Arxiv Computing Research Repository - Cornell University (abs/1206.3552) (literal)

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

• http://arxiv.org/abs/1206.3552 (literal)

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

• 35 (literal)

Note

• PuMa (literal)

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

• Altro (literal)

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

• Computer Science Department, University of Pisa, Italy - ISTI-CNR, Pisa, Italy - Center for Complex Network Research, Northeast University, Boston, USA; CNR-ISTI, Pisa, Italy; Department of Computer Science, Univ. Pisa; (literal)

Titolo

• A classification for community discovery methods in complex networks (literal)

Abstract

• Many real-world networks are intimately organized according to a community structure. Much research effort has been devoted to develop methods and algorithms that can efficiently highlight this hidden structure of a network, yielding a vast literature on what is called today community detection. Since network representation can be very complex and can contain different variants in the traditional graph model, each algorithm in the literature focuses on some of these properties and establishes, explicitly or implicitly, its own definition of community. According to this definition, each proposed algorithm then extracts the communities, which typically reflect only part of the features of real communities. The aim of this survey is to provide a 'user manual' for the community discovery problem. Given a meta definition of what a community in a social network is, our aim is to organize the main categories of community discovery methods based on the definition of community they adopt. Given a desired definition of community and the features of a problem (size of network, direction of edges,multidimensionality, and so on) this review paper is designed to provide a set of approaches that researchers could focus on. The proposed classification of community discovery methods is also useful for putting into perspective the many open directions for further research. (literal)

Prodotto di

• Data Mining, Identificazione e Stima per Sistemi Complessi (ICT.P11.004.001) (Modulo)
• Istituto di scienza e tecnologie dell'informazione \"Alessandro Faedo\" (ISTI) (Istituto)

Autore CNR

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

Insieme di parole chiave

• Keywords of "A classification for community discovery methods in complex networks" (Insieme di parole chiave)

Incoming links:

Prodotto

• Istituto di scienza e tecnologie dell'informazione \"Alessandro Faedo\" (ISTI) (Istituto)
• Data Mining, Identificazione e Stima per Sistemi Complessi (ICT.P11.004.001) (Modulo)

Autore CNR di

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

Insieme di parole chiave di

• Keywords of "A classification for community discovery methods in complex networks" (Insieme di parole chiave)