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.

翻译：社交网络在过去一个世纪中已被多个学科广泛研究，以理解就业率不平等、管理绩效和流行病传播等社会问题。如今，得益于我们在浏览网页或使用社交媒体平台时产生的数字足迹，这些以及更多问题可以在全球范围内进行研究。然而，科学家们常常难以获取此类数据，主要原因在于数据所有权问题；即便在隐私保障下共享，这些数据要么缺乏代表性，要么过于庞大。在本教程中，我们将讨论网络建模的最新进展和未来方向。特别地，我们聚焦于如何利用合成网络研究现实世界中的问题，例如数据隐私、传播动态、算法偏差和排名不平等。首先，我们回顾不同类型的社会网络生成模型，包括节点属性和无标度网络。然后，我们展示如何执行网络选择分析，以表征任何给定真实网络的边形成机制。