| dc.contributor.author | Martínez, Víctor | |
| dc.contributor.author | Berzal Galiano, Fernando | |
| dc.contributor.author | Cubero Talavera, Juan Carlos | |
| dc.date.accessioned | 2019-12-12T12:54:29Z | |
| dc.date.available | 2019-12-12T12:54:29Z | |
| dc.date.issued | 2019-10-31 | |
| dc.identifier.citation | Martínez, V., Berzal, F., & Cubero, J. C. (2019). NOESIS: A Framework for Complex Network Data Analysis. Complexity, 2019. | es_ES |
| dc.identifier.uri | http://hdl.handle.net/10481/58275 | |
| dc.description.abstract | Network data mining has attracted a lot of attention since a large number of real-world problems have to deal with complex
network data. In this paper, we present NOESIS, an open-source framework for network-based data mining. NOESIS features a
large number of techniques and methods for the analysis of structural network properties, network visualization, community
detection, link scoring, and link prediction. e proposed framework has been designed following solid design principles and
exploits parallel computing using structured parallel programming. NOESIS also provides a stand-alone graphical user interface
allowing the use of advanced software analysis techniques to users without prior programming experience. is framework is
available under a BSD open-source software license. | es_ES |
| dc.description.sponsorship | The NOESIS project was partially supported by the Spanish
Ministry of Economy and the European Regional Development
Fund (FEDER), under grant TIN2012–36951, and the
Spanish Ministry of Education under the program “Ayudas
para contratos predoctorales para la formación de doctores
2013” (predoctoral grant BES–2013–064699). | es_ES |
| dc.language.iso | eng | es_ES |
| dc.publisher | Wiley | es_ES |
| dc.rights | Atribución 3.0 España | * |
| dc.rights.uri | http://creativecommons.org/licenses/by/3.0/es/ | * |
| dc.title | NOESIS: A Framework for Complex Network Data Analysis | es_ES |
| dc.type | journal article | es_ES |
| dc.rights.accessRights | open access | es_ES |
| dc.identifier.doi | 10.1155/2019/1439415 | |