Diffusion in Social and Information Networks: Research Problems, Probabilistic Models & Machine Learning Methods

In recent years, there has been an increasing effort on developing realistic models, and learning and inference algorithms to understand, predict, and influence diffusion over networks. This has been in part due to the increasing availability and granularity of large-scale diffusion data, which, in principle, allows for understanding and modeling not only macroscopic diffusion but also microscopic (node-level) diffusion. To this aim, a bottom-up approach has been typically considered, which starts by considering how particular ideas, pieces of information, products, or, more generally, contagions spread locally from node to node apparently at random to later produce global, macroscopic patterns at a network level. However, this bottom-up approach also raises significant modeling, algorithmic and computational challenges which require leveraging methods from machine learning, probabilistic modeling, event history analysis and graph theory, as well as the nascent field of network science. In this tutorial, we will present several diffusion models designed for fine-grained large-scale diffusion data, present some canonical research problem in the context of diffusion, and introduce state-of-the-art algorithms to solve some of these problems, in particular, network estimation, influence estimation and influence control.