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There is now a CONTENT FREEZE for Mercury while we switch to a new platform. It began on Friday, March 10 at 6pm and will end on Wednesday, March 15 at noon. No new content can be created during this time, but all material in the system as of the beginning of the freeze will be migrated to the new platform, including users and groups. Functionally the new site is identical to the old one. webteam@gatech.edu
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You are cordially invited to attend my thesis defense.
Thesis Title: Influence network analysis on social network and critical infrastructure interdependencies
Advisors: Dr. Chip White and Dr. Rachel Cummings
Committee members:
Dr. Andy Sun
Dr. David Goldsman
Dr. Marilyn Brown (School of Public Policy)
Date and Time: 10-12 pm ET, Thursday, July 23, 2020
Meeting URL: https://bluejeans.com/977664675
Meeting ID: 977 664 675 (bluejeans)
Abstract:
Inspired by the social network, the influence network has been proved to be a powerful tool to analyze the influence propagation within a group of entities. In this dissertation, we present three applications of influence networks on social networks and critical infrastructures after a comprehensive review of the previous literature.
In Chapter 2, we review some major results of influence network models and approaches to address and solve the influence maximization problem. In the first section, we give a formal definition of the problem and introduce two major influence network models. In the second section, we will review some major approaches to solve the problems based on the models in Section 1. Finally, in Section 3 we will review other notable methods for this problem.
Chapter 3 demonstrates a sociology-based computational framework for independent cascade (IC) influence networks. The framework is used to calibrate the positive influence of church clergy in spreading HIV/AIDs information in a large metropolitan city. Five experiments are designed to contrast influence with respect to the interaction style between clergy and churchgoers. Competitive (CIC model) and non-competitive (IC model) knowledge dissemination are also analyzed.
We present a two-stage framework to analyze the cascading effects in the cellular station network using the linear threshold influence network in Chapter 4. The communication sector is one of the 16 critical infrastructure sectors identified by The Department of Homeland Security (DHS). Cellular station networks are a vital part of the sector on which almost all private businesses, organization and governments rely. In this chapter, we try to answer the question that if the resources held by policy makers are restricted, how to find the most critical cellular stations whose collapse would affect most cellular stations in the network.
In Chapter 5, we extend the linear threshold influence network model to the multi-layer case and use this new tool in a two-stage framework to analyze the cascading effects in the critical infrastructure interdependency network. This is an extension of chapter 4 where we only explore the single-layer LT influence network. We explore two experiments on the metro Atlanta area and the state of Florida as the applications of the model.
You are cordially invited to attend my thesis defense.
Thesis Title: Influence network analysis on social network and critical infrastructure interdependencies
Advisors: Dr. Chip White and Dr. Rachel Cummings
Committee members:
Dr. Andy Sun
Dr. David Goldsman
Dr. Marilyn Brown (School of Public Policy)
Date and Time: 10-12 pm ET, Thursday, July 23, 2020
Meeting URL: https://bluejeans.com/977664675
Meeting ID: 977 664 675 (bluejeans)
Abstract:
Inspired by the social network, the influence network has been proved to be a powerful tool to analyze the influence propagation within a group of entities. In this dissertation, we present three applications of influence networks on social networks and critical infrastructures after a comprehensive review of the previous literature.
In Chapter 2, we review some major results of influence network models and approaches to address and solve the influence maximization problem. In the first section, we give a formal definition of the problem and introduce two major influence network models. In the second section, we will review some major approaches to solve the problems based on the models in Section 1. Finally, in Section 3 we will review other notable methods for this problem.
Chapter 3 demonstrates a sociology-based computational framework for independent cascade (IC) influence networks. The framework is used to calibrate the positive influence of church clergy in spreading HIV/AIDs information in a large metropolitan city. Five experiments are designed to contrast influence with respect to the interaction style between clergy and churchgoers. Competitive (CIC model) and non-competitive (IC model) knowledge dissemination are also analyzed.
We present a two-stage framework to analyze the cascading effects in the cellular station network using the linear threshold influence network in Chapter 4. The communication sector is one of the 16 critical infrastructure sectors identified by The Department of Homeland Security (DHS). Cellular station networks are a vital part of the sector on which almost all private businesses, organization and governments rely. In this chapter, we try to answer the question that if the resources held by policy makers are restricted, how to find the most critical cellular stations whose collapse would affect most cellular stations in the network.
In Chapter 5, we extend the linear threshold influence network model to the multi-layer case and use this new tool in a two-stage framework to analyze the cascading effects in the critical infrastructure interdependency network. This is an extension of chapter 4 where we only explore the single-layer LT influence network. We explore two experiments on the metro Atlanta area and the state of Florida as the applications of the model.
