Learning the meso-scale organization of complex networks

这个项目旨在模拟和学习在线社交网络的组织。我们将结合数学模型、推理和来自计算社会科学的领域知识，以获得用户群体在网络中扮演的角色的可解释描述。预期的结果是新的数学模型和计算方法，从数据中学习如何最好地将复杂的网络分解为构建块及其相互作用，将连接与功能联系起来。这应该会给对信息如何在社交媒体上传播感兴趣的行业和政策制定者带来好处，包括理解导致政治两极分化和分裂的机制等关键问题。

This project aims to model and learn the organization of online social networks. We will combine mathematical models, inference, and domain knowledge from computational social sciences to obtain interpretable descriptions of the role groups of users play in the network. The expected outcomes are new mathematical models and computational methods that learn from data how to best decompose a complex network into building blocks and their interactions, linking connectivity to function. This should provide benefits to industries and policy makers interested in how information spreads in social media, including the critical questions of understanding the mechanisms contributing to political polarization and fragmentation.