Date of Award
2019
Publisher
North Dakota State University
Document Type
Master's Paper
Degree Awarded
Master of Science (MS)
Program
Computer Science
Department
Computer Science
College
Engineering
Faculty Advisor
Nygard, Kendall
Description
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.
Recommended Citation
Durgapathi, Yashwanth Goud, "Enumeration of Dense Modules in Networks" (2019). Computer Science. 136.
https://digitalcommons.ndsu.edu/computer-sci/136
Rights
NDSU Policy 190.6.2
Rights Link
https://www.ndsu.edu/fileadmin/policy/190.pdf
Handle Identifier
https://hdl.handle.net/10365/29845