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Dynamic models and statistical inference for the diffusion of information insocial networks is an area which has witnessed remarkable progress in the lastdecade due to the proliferation of social networks. Modeling and inference ofdiffusion of information has applications in targeted advertising andmarketing, forecasting elections, predicting investor sentiment and identifyingepidemic outbreaks. This chapter discusses three important aspects related toinformation diffusion in social networks: (i) How does observation bias namedfriendship 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 thestate of information diffusion. (iii) How one can estimate the state of thenetwork induced by information diffusion. The motivation for all three topicsconsidered in this chapter stems from recent findings in network science andsocial sensing. Further, several directions for future research that arise fromthese topics are also discussed.

Information Diffusion in Social Networks: Friendship Paradox based Models and Statistical Inference