Title: Information Diffusion in Social Networks: Friendship Paradox based Models and Statistical Inference,
Abstract: Dynamic models and statistical inference for the diffusion of information in
social networks is an area which has witnessed remarkable progress in the last
decade due to the proliferation of social networks. Modeling and inference of
diffusion of information has applications in targeted advertising and
marketing, forecasting elections, predicting investor sentiment and identifying
epidemic outbreaks. This chapter discusses three important aspects related to
information diffusion in social networks: (i) How does observation bias named
friendship paradox (a graph theoretic consequence) and monophilic contagion
(influence of friends of friends) affect information diffusion dynamics. (ii)
How can social networks adapt their structural connectivity depending on the
state of information diffusion. (iii) How one can estimate the state of the
network induced by information diffusion. The motivation for all three topics
considered in this chapter stems from recent findings in network science and
social sensing. Further, several directions for future research that arise from
these topics are also discussed.