Home > Events > Oral Candidacy - Corey Pennycuff

Oral Candidacy - Corey Pennycuff

Start: 4/28/2017 at 1:00PM
End: 4/28/2017 at 4:00PM
Location: 100 Stinson Remick
Add to calendar:
iCal vCal

Corey Pennycuff
Oral Candidacy


April 28, 2017        1:00 pm       100 Stinson Remick


Adviser:  Dr. Tim Weninger
Committee Members:
Dr. Larry Holder        Dr. Tijana Milenkovic        Dr. Dong Wang

Title: 

Synchronous Grammars For HyperEdge Replacement Graphs

Abstract:

Data and data collection is a fundamental tenet of science.  As technology has advanced, so has our ability to record, store, and analyze data en masse.  While there has always been interest in networks, and graphs have been studied for centuries, it is only recently that we have developed the tools to chronicle the timely, complex interactions of multiple, varied sources.  The advent of the Internet, for example, and the ubiquity of its use in people's personal and professional lives, further emphasizes the importance of understanding the dynamics of the development of graphs in the form of social networks.  The frontier of this field is in understanding the network data that has been collected.

One way to understand data is to create a model that mimics the behavior of that data.  Models are often tailored to a specific aspect of the understanding of the data and are judged according to the metrics of that field which have been deemed important.  In graph theory, we have many different models for generating graphs, each with varying degrees of fidelity to the source data.  One thing that they all have in common, however, is that they focus on creating a single snapshot of the graph.  We know, however, that graphs are often not static; they change and grow and shrink, and this feature is absent from most generative models.

My work will focus on using time-series data and the Hyperedge Replacement Grammars graph generation model to model graphs as they grow and change over time.  This will be done by developing a Synchronous Grammar model that extends the Hyperedge Replacement Grammar, which itself has been shown to perform well as a graph generation model.  I will then mine the resulting growth patterns that are observed in order to better understand the process surrounding evolving topologies.