Ph.D. Defense of Dissertation: Myungcheol Doo

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Title: Spatial and Social Diffusion of Information and Influence: Models and Algorithms

Myungcheol Doo
School of Computer Science
College of Computing
Georgia Institute of Technology

When: Apr 23 Monday 11AM
Where: KACB 3402


  • Dr. Ling Liu (Advisor, School of Computer Science, Georgia Institute of Technology)
  • Dr. Sham Navathe (School of Computer Science, Georgia Institute of Technology)
  • Dr. Edward Omiecinski (School of Computer Science, Georgia Institute of Technology)
  • Dr. Calton Pu (School of Computer Science, Georgia Institute of Technology)
  • Dr. Lakshmish Ramaswamy (Department of Computer Science, The University of Georgia)

With the ubiquitous connectivity, we are entering an information age where people are connected all the time and information/influence is diffused continuously. This dissertation research is dedicated towards effective and scalable models and algorithms for effective diffusion of spatial and social influence.

This dissertation research has made three unique contributions.

  • First, we develop an activity driven and self-configurable social influence model and a suite of computational algorithms to compute and rank social network nodes in terms of their activity-based influence ranks. Our model improves the diffusion effectiveness based on multiple spatial and social parameters, such as diffusion linkage, diffusion location, diffusion energy (heat), diffusion coverage, to name a few.
  • Second, we extend our activity-based social influence model by incorporating probabilistic diffusion of influence based on activities, capturing a spectrum of diffusion states from active to stale mode.
  • We also examine the effectiveness of incentives such as multi-scale reward points popular in many business settings in stimulating social and spatial dissemination of information and influences.  

In this defense, I will give an overview of my dissertation research and focus on the design and evaluation of our activity-based probabilistic approach to modeling social influence and designing influence ranking algorithms. We will show how incentives such as multi-scale rewards may impact on the efficacy of activity based social influence.  


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