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dc.contributor.authorDurgapathi, Yashwanth Goud
dc.description.abstractTo analyze large networks in various research fields including biology, sociology, and web mining, detection of dense modules (a.k.a. clusters) is a crucial step. Though numerous methods have been proposed to this aim, they often lack mathematical rigor. Namely, there is no guarantee that all dense modules are detected. Here, a novel method for enumerating all dense modules is presented as well as a python program implementing this algorithm. Module detection plays a significant role in variety of modern systems to understand their functionality in depth for example in Biology. In Biology many data bases for the protein-protein interactions are provided (PPI) and the analysis of a PPI's is useful to determine the functionality of known genes or functional annotation of previously unknown genes we can often come across dense clusters in these databases which likely to represent protein complexes.en_US
dc.publisherNorth Dakota State Universityen_US
dc.rightsNDSU Policy 190.6.2
dc.titleEnumeration of Dense Modules in Networksen_US
dc.typeMaster's paperen_US
dc.date.accessioned2019-06-14T20:30:34Z
dc.date.available2019-06-14T20:30:34Z
dc.date.issued2019en_US
dc.identifier.urihttps://hdl.handle.net/10365/29845
dc.subject.lcshData mining.
dc.subject.lcshComputer networks.
dc.subject.lcshGraphic methods.
dc.subject.lcshGraph algorithms.
dc.rights.urihttps://www.ndsu.edu/fileadmin/policy/190.pdf
ndsu.degreeMaster of Science (MS)en_US
ndsu.collegeEngineeringen_US
ndsu.departmentComputer Scienceen_US
ndsu.programComputer Scienceen_US
ndsu.advisorNygard, Kendall


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