Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/52067
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dc.contributor.authorRisan.
dc.date.accessioned2013-04-22T03:57:24Z
dc.date.available2013-04-22T03:57:24Z
dc.date.copyright2013en_US
dc.date.issued2013
dc.identifier.urihttp://hdl.handle.net/10356/52067
dc.description.abstractThere are various community detection algorithms which that have been developed. Among them, Louvain method is the most widely used algorithm because of its simplicity and good performance. The goal of this project is to improve an existing parallel implementation of community detection algorithm based on Louvain method that works on multiple GPU. This project empirically studies existing partitioning methods, memory and running time optimization. As the result of the studies, a new partitioning method was proposed to decrease the running time of overall algorithm. The functionality was also expanded by allowing weighted network as input. In addition, the running time of modularity computation was also improved.en_US
dc.format.extent57 p.en_US
dc.language.isoenen_US
dc.rightsNanyang Technological University
dc.subjectDRNTU::Engineering::Computer science and engineeringen_US
dc.titleLarge-scale community detection in social networksen_US
dc.typeFinal Year Project (FYP)en_US
dc.contributor.supervisorStephen John Turneren_US
dc.contributor.schoolSchool of Computer Engineeringen_US
dc.description.degreeBachelor of Engineering (Computer Science)en_US
dc.contributor.organizationA*STAR Institute of High Performance Computing (IHPC)en_US
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Appears in Collections:SCSE Student Reports (FYP/IA/PA/PI)
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