A multi-level analysis of information and supply flows in social and business networks

Open Access
Author:
Zhao, Kang
Graduate Program:
Information Sciences and Technology
Degree:
Doctor of Philosophy
Document Type:
Dissertation
Date of Defense:
April 20, 2012
Committee Members:
  • John Yen, Dissertation Advisor
  • Reka Z Albert, Committee Member
  • C Lee Giles, Committee Member
  • Akhil Kumar, Committee Member
Keywords:
  • Social networks
  • supply-chain networks
  • inter-organizational networks
  • information flow
  • influence
  • online health community.
Abstract:
Networks are ubiquitous in both the physical world and the cyberspace. They enable the flow of information, materials, services, etc. The common thread running through my dissertation is the macro-level and micro-level analysis of information and supply flows in social and business networks. The overarching research question is "How do network flows relate to network structures and individual entities' behaviors in networks?" My dissertation approaches this question in the context of supply-chain networks, online social networks, and inter-organizational networks. Specifically, I explore how changes in supply-chain network topologies affect supply flows, and propose strategies to improve supply-chain networks' robustness against disruptions. I analyze how the flow of information through individuals' interactions influences their sentiment in online health communities, and utilize the sentimental influence to identify influential users. I model how the flow of information via organizations' interactions impacts the structure of their collaboration network, and provide suggestions on how to promote collaboration. To support the research, the dissertation uses various computational and quantitative methodologies, such as network analysis and modeling, data and text mining, agent-based simulation, and statistical analysis. The goal of my dissertation research is to achieve a deeper understanding of network flows, better prediction of network performance and individual behaviors, as well as new insights into ways to improve the design, management, and utilization of networks. Specifically, the outcome of this dissertation has implications for network disruption management, health care, online community building, humanitarian relief, and so on.