Mining the temporal dimension of the information propagation (Articolo in rivista)

Type

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

Label

• Mining the temporal dimension of the information propagation (Articolo in rivista) (literal)

Anno

• 2009-01-01T00:00:00+01:00 (literal)

Alternative label

• Berlingerio M.; Coscia M.; Giannotti F. (2009)
  Mining the temporal dimension of the information propagation
  in Lecture notes in computer science
  (literal)

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

• Berlingerio M.; Coscia M.; Giannotti F. (literal)

Pagina inizio

• 237 (literal)

Pagina fine

• 248 (literal)

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

• 5772 (literal)

Rivista

• Lecture notes in computer science (Rivista)

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

• In: IDA 2009 - Advances in Intelligent Data Analysis VIII. 8th International Symposium on Intelligent Data Analysis (Lyon, France, August 31 - September 2 2009). Proceedings, pp. 237 - 248. (Lecture Notes in Computer Science, vol. 5772). Springer-Verlag, 2009. (literal)

Note

• ISI Web of Science (WOS) (literal)

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

• CNR-ISTI, Pisa, Department of Computer Science, Univ. Pisa (literal)

Titolo

• Mining the temporal dimension of the information propagation (literal)

Abstract

• In the last decade, Social Network Analysis has been a field in which the effort devoted from several researchers in the Data Mining area has increased very fast. Among the possible related topics, the study of the information propagation in a network attracted the interest of many researchers, also from the industrial world. However, only a few answers to the questions \"How does the information propagates over a network, why and how fast?\" have been discovered so far. On the other hand, these answers are of large interest, since they help in the tasks of finding experts in a network, assessing viral marketing strategies, identifying fast or slow paths of the information inside a collaborative network. In this paper we study the problem of finding frequent patterns in a network with the help of two different techniques: TAS (Temporally Annotated Sequences) mining, aimed at extracting sequential patterns where each transition between two events is annotated with a typical transition time that emerges from input data, and Graph Mining, which is helpful for locally analyzing the nodes of the networks with their properties. Finally we show preliminary results done in the direction of mining the information propagation over a network, performed on two well known email datasets, that show the power of the combination of these two approaches. (literal)

Prodotto di

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

Autore CNR

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

Insieme di parole chiave

• Parole chiave di "Mining the temporal dimension of the information propagation" (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

• Lecture notes in computer science (Rivista)

Insieme di parole chiave di

• Parole chiave di "Mining the temporal dimension of the information propagation" (Insieme di parole chiave)