Automatic multi-partite graph generation from arbitrary data

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To access the full text documents, please follow this link: http://hdl.handle.net/2117/26446

dc.contributor	Universitat Politècnica de Catalunya. Departament d'Arquitectura de Computadors
dc.contributor	Universitat Politècnica de Catalunya. DAMA-UPC - Data Management Group
dc.contributor.author	Álvarez García, Sandra
dc.contributor.author	Baeza Yates, Ricardo
dc.contributor.author	Brisaboa, Nieves R.
dc.contributor.author	Larriba Pey, Josep
dc.contributor.author	Pedreira, Oscar
dc.date	2014-08-22
dc.identifier.citation	Álvarez, S. [et al.]. Automatic multi-partite graph generation from arbitrary data. "Journal of systems and software", 22 Agost 2014, vol. 94, p. 72-86.
dc.identifier.citation	0164-1212
dc.identifier.citation	10.1016/j.jss.2014.03.022
dc.identifier.uri	http://hdl.handle.net/2117/26446
dc.language.iso	eng
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	Àrees temàtiques de la UPC::Informàtica::Sistemes d'informació
dc.subject	Graphic methods
dc.subject	Graph processing and analysis
dc.subject	Automatic graph generation
dc.subject	Mètodes gràfics
dc.title	Automatic multi-partite graph generation from arbitrary data
dc.type	info:eu-repo/semantics/publishedVersion
dc.type	info:eu-repo/semantics/article
dc.description.abstract	In this paper we present a generic model for automatic generation of basic multi-partite graphs obtained from collections of arbitrary input data following user indications. The paper also presents GraphGen, a tool that implements this model. The input data is a collection of complex objects composed by a set or list of heterogeneous elements. Our tool provides a simple interface for the user to specify the types of nodes that are relevant for the application domain in each case. The nodes and the relationships between them are derived from the input data through the application of a set of derivation rules specified by the user. The resulting graph can be exported in the standard GraphML format so that it can be further processed with other graph management and mining systems. We end by giving some examples in real scenarios that show the usefulness of this model.
dc.description.abstract	Peer Reviewed

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