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Mining Communities from Multi-Layered Graphs

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dc.description.abstract Identifying 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.title Mining Communities from Multi-Layered Graphs en_US
dc.date.accessioned 2013-06-20T15:09:32Z
dc.date.available 2013-06-20T15:09:32Z
dc.date.issued 2013-06-20
dc.identifier.uri http://hdl.handle.net/10365/23023
dc.thesis.degree Paper (M.S.)--North Dakota State University, 2013. en_US
dc.contributor.advisor Salem, Saeed
dc.subject.lcsh Data mining -- Graphic methods. en_US
dc.subject.lcsh Social networks -- Data processing.
dc.subject.lcsh Computer algorithms.
dc.creator.author Chao, Meng
dc.degree.departmentCollege Master of Science / Computer Science, College of Science and Mathematics, 2013.
dc.date.created 2013

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