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dc.contributor.authorChao, Meng
dc.description.abstractIdentifying communities from networks has been a subject of great interest in Biological and Social network analysis. Finding communities can help with tasks such as identifying and fighting disease. Using graphs to represent networks and identifying dense subgraphs as communities within these graphs is an increasingly important area of study. Many of the same entities can be found in multiple networks, each representing a different type of relationship. These graphs capturing different relationships between the same entities can be combined into a single graph called a “multi-layered graph”. By finding dense subgraphs containing the same entities within multiple layers of the multi-layered graph, we can increase the confidence these dense subgraphs are communites. This paper has developed an algorithm that takes multi-layered graphs and employs quasi-clique based community discovery for extracting communities. Experimental results on real co-authorship networks show that the proposed approach discover communities that have dense interactions.en_US
dc.publisherNorth Dakota State Universityen_US
dc.rightsNDSU Policy 190.6.2
dc.titleMining Communities from Multi-Layered Graphsen_US
dc.typeMaster's paperen_US
dc.date.accessioned2013-06-20T15:09:32Z
dc.date.available2013-06-20T15:09:32Z
dc.date.issued2013
dc.identifier.urihttp://hdl.handle.net/10365/23023
dc.subject.lcshSocial networks -- Data processing.en_US
dc.subject.lcshComputer algorithms.en_US
dc.subject.lcshData mining -- Graphic methods.en_US
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.advisorSalem, Saeed


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