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PROXNET

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Cerrado

Modelling Complex Networks Through Graph Editing Problems

The goal of the PROXNET project, which will be realised during the mobility of Christophe Crespelle in the Algorithms group of Bergen, is to open a new way for analysing, modelling and managing complex networks, through graph edit... ver más

31/01/2020

HVL

208K€

Presupuesto del proyecto: 208K€

Líder del proyecto

HOGSKULEN PA VESTLANDET No se ha especificado una descripción o un objeto social para esta compañía.

Fecha límite participación Sin fecha límite de participación.

Financiación concedida El organismo H2020 notifico la concesión del proyecto el día 2020-01-31

MSCA-IF-2016: Individual Fellowships

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1 Participantes

HVL

208.40K€ | Lider

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Duración del proyecto: 34 meses Fecha Inicio: 2017-03-22
Fecha Fin: 2020-01-31

Líder del proyecto

HOGSKULEN PA VESTLANDET No se ha especificado una descripción o un objeto social para esta compañía.

Presupuesto del proyecto 208K€

Fecha límite de participación Sin fecha límite de participación.

Descripción del proyecto The goal of the PROXNET project, which will be realised during the mobility of Christophe Crespelle in the Algorithms group of Bergen, is to open a new way for analysing, modelling and managing complex networks, through graph editing problems. The reason why these networks are said to be complex is that they are loosely structured, due to the part of uncertainty and randomness they contain. On the other hand, the real-world context where they come from strongly constrains their organisation and gives them some specific structure. The difficulty in retrieving this structure is that it is altered by the noise resulting from the uncertainty and randomness that these networks contain. In the PROXNET project, we will retrieve the hidden structures of complex networks thanks to graph editing problems, which consist in changing some adjacencies of the graph in order to obtain a desired property. We will develop the algorithms necessary to solve graph editing problems relevant to our approach on huge instances of graphs, we will apply them to real-world datasets and use the results obtained in order to design new models of complex networks. One key theoretic challenge in this context is to optimally solve the editing problems considered, which are computationally difficult (NP-hard). A very promising technique to do so is preprocessing. It consists in reducing the size of the instance by applying some reduction rules, in such a way that the optimal solution on the reduced instance is the same as the one on the original instance. The Algorithms group of the University of bergen is a world-wide leader on this topic which will constitute the main training action of Christophe Crespelle during his mobility.

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