In this chapter, we provide an overview of recent advances in data-driven and theory-informed complex models of social networks and their potential in understanding societal inequalities and marginalization. We focus on inequalities arising from networks and network-based algorithms and how they affect minorities. In particular, we examine how homophily and mixing biases shape large and small social networks, influence perception of minorities, and affect collaboration patterns. We also discuss dynamical processes on and of networks and the formation of norms and health inequalities. Additionally, we argue that network modeling is paramount for unveiling the effect of ranking and social recommendation algorithms on the visibility of minorities. Finally, we highlight the key challenges and future opportunities in this emerging research topic.

翻译：在本章中,我们概述了社会网络的数据驱动和理论知情的综合模型及其在理解社会不平等和边缘化方面的潜力的最新进展。我们侧重于网络和基于网络的算法所产生的不平等,以及它们如何影响少数群体。我们特别审查了偏见如何以同种和混合方式形成大小社会网络,如何影响对少数群体的看法,如何影响协作模式。我们还讨论了动态过程、网络以及规范与健康不平等的形成。此外,我们认为,网络建模对于揭示排名和社会推荐算法对少数群体可见度的影响至关重要。最后,我们强调了这个新兴研究课题的主要挑战和今后的机会。