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Community Detection In Large Networks
| dc.contributor.author | Andrews, June | en_US |
| dc.date.accessioned | 2013-01-31T19:44:03Z | |
| dc.date.available | 2017-12-20T07:00:23Z | |
| dc.date.issued | 2012-08-20 | en_US |
| dc.identifier.other | bibid: 7959769 | |
| dc.identifier.uri | https://hdl.handle.net/1813/31046 | |
| dc.description.abstract | Graphs are used to represent various large and complex networks in scientific applications. In order to understand the structure of these graphs, it is useful to treat a set of nodes with similar characteristics as one community and analyze the community's behavior as a whole. Finding all such communities within the graph is the object of community detection. In our research, we compare dozens of existing community detection methods and develop a new class of algorithms for finding communities. | en_US |
| dc.language.iso | en_US | en_US |
| dc.subject | community detection | en_US |
| dc.subject | social networks | en_US |
| dc.title | Community Detection In Large Networks | en_US |
| dc.type | dissertation or thesis | en_US |
| thesis.degree.discipline | Applied Mathematics | |
| thesis.degree.grantor | Cornell University | en_US |
| thesis.degree.level | Doctor of Philosophy | |
| thesis.degree.name | Ph. D., Applied Mathematics | |
| dc.contributor.chair | Hopcroft, John E | en_US |
| dc.contributor.committeeMember | Kleinberg, Jon M | en_US |
| dc.contributor.committeeMember | Strogatz, Steven H | en_US |
