MIGRANT

Mining Graphs and Networks: a Theory-based approach

 Coordinatore KATHOLIEKE UNIVERSITEIT LEUVEN 

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 Nazionalità Coordinatore Belgium [BE]
 Totale costo 1˙716˙066 €
 EC contributo 1˙716˙066 €
 Programma FP7-IDEAS-ERC
Specific programme: "Ideas" implementing the Seventh Framework Programme of the European Community for research, technological development and demonstration activities (2007 to 2013)
 Code Call ERC-2009-StG
 Funding Scheme ERC-SG
 Anno di inizio 2009
 Periodo (anno-mese-giorno) 2009-12-01   -   2015-05-31

 Partecipanti

# participant  country  role  EC contrib. [€] 
1    KATHOLIEKE UNIVERSITEIT LEUVEN

 Organization address address: Oude Markt 13
city: LEUVEN
postcode: 3000

contact info
Titolo: Dr.
Nome: Jan
Cognome: Ramon
Email: send email
Telefono: -968972
Fax: -327966

BE (LEUVEN) hostInstitution 1˙716˙066.00
2    KATHOLIEKE UNIVERSITEIT LEUVEN

 Organization address address: Oude Markt 13
city: LEUVEN
postcode: 3000

contact info
Titolo: Dr.
Nome: Stijn
Cognome: Delauré
Email: send email
Telefono: +32 16 320 944
Fax: +32 16 324 198

BE (LEUVEN) hostInstitution 1˙716˙066.00

Mappa


 Word cloud

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data    standard    mining    world    insights    structured    learning    foundations    theory    techniques    interesting    graph   

 Obiettivo del progetto (Objective)

'In this project we aim at formulating enhancing theoretical foundations for the emerging field of graph mining. Graph mining is the field concerned with extracting interesting patterns and knowledge from graph or network structured data, such as can be found in chemistry, bioinformatics, the world wide web, social networks etc. Recent work has shown that many standard data mining techniques can be extended to structured data and can yield interesting results, but also that when applied to complex real-world data, these standard techniques often become computationally intractable. In this project we aim at providing a better understanding of the complexity of the tasks considered in the field of graph mining, and at proposing techniques to better exploit the properties of the data. To this aim, we will bring together insights from the fields of data mining, graph theory, learning theory and different application fields, and add our own original contributions. Key features of the methodology include the ground-breaking integration of insights from graph theory in data mining and learning approaches, the development of efficient prototype algorithms, and the interdisciplinary collaboration with application domain experts to validate the practical value of the work, This potential impact of this project is significant, as it will be the first systematic study of the theory of graph mining, it will provide foundations on which later research can build further and it will have applications in the many domains with complex data.'

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