Modeling Social Networks with Random and Fuzzy Graphs
Presentation Type
Poster/Portfolio
Presenter Major(s)
Statistics, Mathematics
Mentor Information
Jiyeon Suh
Department
Mathematics
Location
Henry Hall Atrium 40
Start Date
11-4-2012 9:00 AM
Abstract
In this project, random weight graph models are extended to the fuzzy case, where fuzzy probability theory drives the stochastic process. To illustrate, suppose that an edge in a weighted graph is known to exist between two particular vertices but the strength of that edge is unclear. To determine the strength of this edge, we find the conditional expectation of a fuzzy random variable conditioned on the strength of mutual friends shared by the two vertices. The calculation of expected weight in this manner drives the stochastic process as a new vertex is connected randomly to the graph with each iteration. We discuss the efficacy of our approach as a modeling tool, interesting growth characteristics of the model, and possible modifications to the process.
Modeling Social Networks with Random and Fuzzy Graphs
Henry Hall Atrium 40
In this project, random weight graph models are extended to the fuzzy case, where fuzzy probability theory drives the stochastic process. To illustrate, suppose that an edge in a weighted graph is known to exist between two particular vertices but the strength of that edge is unclear. To determine the strength of this edge, we find the conditional expectation of a fuzzy random variable conditioned on the strength of mutual friends shared by the two vertices. The calculation of expected weight in this manner drives the stochastic process as a new vertex is connected randomly to the graph with each iteration. We discuss the efficacy of our approach as a modeling tool, interesting growth characteristics of the model, and possible modifications to the process.