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Markov chain monte carlo methods applied to big graphs clustering
dc.contributor | Arias Vicente, Marta |
dc.contributor | Clemencon, Stephan |
dc.contributor.author | Mullor, Elsa |
dc.contributor.other | Universitat Politècnica de Catalunya. Departament d'Estadística i Investigació Operativa |
dc.date.accessioned | 2015-11-13T16:45:04Z |
dc.date.available | 2015-11-13T16:45:04Z |
dc.date.issued | 2015-07-10 |
dc.identifier.uri | http://hdl.handle.net/2117/79264 |
dc.description.abstract | We propose two new method that implements community detection algorithms introducing stochastic process. The stochasticity aims at enabling the methods to exit local minima. Besides we expect to obtain better efficiency and scalability. We also discuss performances and analyse the methods' behave. |
dc.language.iso | eng |
dc.publisher | Universitat Politècnica de Catalunya |
dc.subject | Àrees temàtiques de la UPC::Informàtica |
dc.subject.lcsh | Graph theory |
dc.subject.other | Graphs |
dc.subject.other | Community detection |
dc.subject.other | clustering |
dc.subject.other | Markov chains Monte Carlo |
dc.subject.other | Louvain |
dc.subject.other | Stochasticity |
dc.subject.other | Modularity. |
dc.title | Markov chain monte carlo methods applied to big graphs clustering |
dc.type | Master thesis |
dc.subject.lemac | Grafs, Teoria de |
dc.identifier.slug | 108987 |
dc.rights.access | Open Access |
dc.date.updated | 2015-07-14T04:01:01Z |
dc.audience.educationlevel | Màster |
dc.audience.mediator | Facultat d'Informàtica de Barcelona |
dc.audience.degree | MÀSTER UNIVERSITARI EN INNOVACIÓ I RECERCA EN INFORMÀTICA (Pla 2012) |
dc.contributor.covenantee | École nationale supérieure des télécommunications |