Date of Award

2014

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

Salem, Saeed

Description

Data mining techniques have an important implication in social and biological network analysis, were we're interested in finding related complexes and communities. A modern paradigm for solving this problem involves finding densely connected interacting members such as bound proteins in a PPI. It's important to also consider the properties of members. In the context of social networks, we might be interested in finding groups of friends of similar age and sharing common interests. This information can lead to better targeting for advertising and suggestions. In this paper, we introduce an algorithm that can be applied to mining entity- relationship networks. Our approach discovers relevant subnetworks by considering density among entities as well as their similar attribute properties. We apply two distinct methods of forming subnetworks in order to find as many relevant complexes as possible. In addition, we supply techniques for summarization and reduction of nearly-redundant subnetworks in the results.

Rights

NDSU Policy 190.6.2

Rights Link

https://www.ndsu.edu/fileadmin/policy/190.pdf

Handle Identifier

https://hdl.handle.net/10365/23675

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