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Diffusion Based Influence Maximization in GOLAP

  • Author(s): Kim, Mira
  • Advisor(s): Sheu, Phillip
  • et al.

Influence analysis is one of the most important research in social network. Specifically, more and more researchers and advertisers are interested in the area of influence maximization. The concept of influence among people or organizations has been the core basis for making business decisions as well as performing everyday social activities.

In this research, we begin by extending a new influence diffusion model (IDM). We incorporate colors and additional node constraints. By adding colors and constraints for different types of nodes in a graph, we are able to solve many practical applications as well as theoretical challenges. We would be able to answer complex queries on multi-dimensional graph such as ‘find at most 2 most important genes that are related to lung disease and heart disease’. More specifically, we discuss the following variations of IM-IDM: Colorblind IM-IDM, Colored IM-IDM, Colored IM-IDM with constraints, lossy IM-IDM as well as colored edge IM-IDM. We also present our experiment results to prove the effectiveness of our model and algorithms.

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