Diffusion Models for Social Analysis, Influence and Learning

Social networks are complicated by millions of users
interacting and creating massive amounts of content. The
problem is that any unobservable changes in network structure
can result in dramatic swings in the spread of new ideas and
behaviors between users. This diffusion process leads to
numerous latent information that can be extracted, analyzed,
and used in different applications, including market forecasting,
rumor control, disease modeling, and opinion monitoring.
Furthermore, mining social media big data helps to ease
tracking propagated data and trends across the world. In this
article, we address the study of diffusion models in social
networks. We discuss three significant categories of diffusion
models: contagion, social influence, and social learning models
with different enhancements applied to improve performance.
The aim is to study diffusion models in social networks to
effectively understand how innovation and information spread
over individuals and predict future trends. Also, identifying the
most influential users in social networks is addressed to help
spread knowledge faster and prevent harmful content like
viruses or bad online behavior from spreading.