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

## TL;DR

This paper explores how the friendship paradox and monophilic contagion influence information spread in social networks, examining structural adaptations and state estimation methods, with implications for marketing, forecasting, and epidemic detection.

## Contribution

It introduces models and inference techniques that incorporate friendship paradox effects and adaptive network structures in social information diffusion.

## Key findings

- Friendship paradox significantly biases diffusion observations.
- Adaptive network models improve diffusion state estimation.
- Analysis of influence of friends of friends on information spread.

## 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.