  Social networks have been widely studied over the last century from multiple
disciplines to understand societal issues such as inequality in employment
rates, managerial performance, and epidemic spread. Today, these and many more
issues can be studied at global scale thanks to the digital footprints that we
generate when browsing the Web or using social media platforms. Unfortunately,
scientists often struggle to access to such data primarily because it is
proprietary, and even when it is shared with privacy guarantees, such data is
either no representative or too big. In this tutorial, we will discuss recent
advances and future directions in network modeling. In particular, we focus on
how to exploit synthetic networks to study real-world problems such as data
privacy, spreading dynamics, algorithmic bias, and ranking inequalities. We
start by reviewing different types of generative models for social networks
including node-attributed and scale-free networks. Then, we showcase how to
perform a network selection analysis to characterize the mechanisms of edge
formation of any given real-world network.
