| dc.contributor.author | Durgapathi, Yashwanth Goud | |
| dc.description.abstract | To 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.publisher | North Dakota State University | en_US |
| dc.rights | NDSU Policy 190.6.2 | |
| dc.title | Enumeration of Dense Modules in Networks | en_US |
| dc.type | Master's paper | en_US |
| dc.date.accessioned | 2019-06-14T20:30:34Z | |
| dc.date.available | 2019-06-14T20:30:34Z | |
| dc.date.issued | 2019 | en_US |
| dc.identifier.uri | https://hdl.handle.net/10365/29845 | |
| dc.subject.lcsh | Data mining. | |
| dc.subject.lcsh | Computer networks. | |
| dc.subject.lcsh | Graphic methods. | |
| dc.subject.lcsh | Graph algorithms. | |
| dc.rights.uri | https://www.ndsu.edu/fileadmin/policy/190.pdf | |
| ndsu.degree | Master of Science (MS) | en_US |
| ndsu.college | Engineering | en_US |
| ndsu.department | Computer Science | en_US |
| ndsu.program | Computer Science | en_US |
| ndsu.advisor | Nygard, Kendall | |
