Publication Date
2018
Document Type
Thesis
Committee Members
Tanvi Banerjee (Committee Member), Derek Doran (Committee Chair), Fred Garber (Committee Member)
Degree Name
Master of Science (MS)
Abstract
Sociotechnological and geospatial processes exhibit time varying structure that make insight discovery challenging. This paper presents statistical model of systems with seasonal dynamics, modeled as a dynamic network, to address this challenge. It assumes the probability of edge formations depend on a type assigned to incident nodes and the current time. Time dependencies are modeled by unique seasonal processes. The model is studied on several synthetic and real datasets. Superior fidelity of this model on seasonal datasets compared to existing network models, while being able to remain equally accurate for networks with randomly changing structure, is shown. The model is found to be twice as accurate at predicting future edge counts over competing models on New York City taxi trips, U.S. airline flights, and email communication within the Enron company. An anomaly detection use case for the model is shown for NYC traffic dynamics and email communications between Enron employees.
Page Count
61
Department or Program
Department of Computer Science and Engineering
Year Degree Awarded
2018
Copyright
Copyright 2018, all rights reserved. My ETD will be available under the "Fair Use" terms of copyright law.
