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Activity Number: 184
Type: Contributed
Date/Time: Monday, August 5, 2013 : 10:30 AM to 12:20 PM
Sponsor: IMS
Abstract - #310297
Title: Marketing on Dynamical Random Networks and Related Inference
Author(s): Daniel Saxton*+ and Anand Vidyashankar
Companies: and George Mason University
Keywords: scale-free networks ; preferential attachment ; fitness ; hub ; power-law

Statistical methods for network data is an emerging field with intense focus from several statisticians. However, statistical evaluation of features of dynamical networks and their influence on marketing strategies is an important and challenging problem that has not been investigated. In this talk, we will provide several probabilistic models for describing cascade dynamics and statistical methods to evaluate the parameters of the cascade processes and the networks. Specifically, we will show that in a variety of models used for describing cascade dynamics, the degree distribution of the network exhibits a power-law. We will also characterize the growth of the degrees of nodes. Using these results, we develop a principled approach to estimating the node influence, predicting the link structure, and identifying the community structure within the network.

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