Learning the meso-scale organization of complex networks

学习复杂网络的中尺度组织

• 批准号: DP240100872
• 负责人: Prof Eduardo Altmann
• 金额: $ 35.06万
• 依托单位: The University of Sydney
• 依托单位国家: 澳大利亚
• 项目类别: Discovery Projects
• 财政年份: 2024
• 资助国家: 澳大利亚
• 起止时间: 2024-01-01 至 2026-12-31
• 项目状态: 未结题
• 来源: https://dataportal.arc.gov.au/RGS/Web/Grants/DP240100872
• 关键词: Learning; meso; scale; organization; complex

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.

这个项目的目的是建模和学习在线社交网络的组织。我们将结合联合收割机的数学模型，推理，并从计算社会科学领域的知识，以获得用户在网络中扮演的角色组的可解释的描述。预期的成果是新的数学模型和计算方法，这些模型和方法可以从数据中学习如何最好地将复杂网络分解为构建块及其相互作用，将连接性与功能联系起来。这应该会给那些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的关键问题。