Author

Guangying Hua

Date of Award

2011

Document Type

Dissertation

Degree Name

PhD in Business

Department

Department of Mathematical Sciences: Business Analytics

First Advisor

Dominique Haughton

Second Advisor

Richard J. Cleary

Third Advisor

Greta Pangborn

Fourth Advisor

Jennifer Jie Xu

Abstract

As network research receives more and more attention from both academic researchers and practitioners, network analysis has become a fast growing field attracting many researchers from diverse fields such as physics, computer science, and sociology. This dissertation provides a review of theory and research on different real data sets from the network perspective. The focus is primarily on the structure and dynamics of social networks.

This dissertation is divided into three essays. The first essay examines a professional online community, and to the best of our knowledge, it is the first paper to address the social network of physicians. The detailed exploratory study and structure analysis show the main characteristics of the network. Moreover, we investigate the correlation between demographic information and physicians' online activities. The findings shed light on the existing literature about the analysis of social media and professional social networking sites.

The second essay presents a model to find overlapping communities in networks. Our approach is based on clique percolation method, which breaks the assumption that each node can only belong to one cluster. Thus, it enables the analysis of a node's multiple roles in the network. Content analysis is conducted as a way to validate the community mining results and to help us better understands the community structure and dynamics.

The third essay investigates the U.S. air transportation network. It explores the dynamics of the U.S. air transportation system and how it evolves over time. The mechanism driving the evolution of the network is proposed, examined and tested in the study. We find that aging effect and preferential attachment are the two mechanisms driving the evolution of the U.S. air transportation network. The network evolution model can be extended to analyze other networks as well.

In summary, my study on SNA applies an interdisciplinary perspective to analyze real network data. The dissertation empirically analyzes data from different fields, from transportation to social media. Methodologically, a new method is proposed to uncover the overlapping communities in the network. The examination of theoretical concepts, such as reciprocity and assortativity, are presented in the dissertation.

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