ICES: Small: Discovering Fundamental Structural and Behavioral Laws of Social Networks

ICES：小：发现社交网络的基本结构和行为规律

• 批准号: 1216082
• 负责人: Alexander Nikolaev
• 金额: $ 10万
• 依托单位: SUNY at Buffalo
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-09-01 至 2014-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1216082&HistoricalAwards=false
• 关键词: ICES; Small; Discovering; Fundamental; Structural

For centuries, the discovery of scientific laws has been driven by empirical investigation of natural phenomena. However, human capabilities of relationship discovery get strained in multi-dimensional spaces, which in particular may explain why we have not seen any fundamental laws postulated for the structure or behavior of such complex constructs as social networks.This project will exploit the methodological toolbox based on symbolic regression with the objective to assist the human mind in simultaneously searching through multiple social network datasets and detecting stable relationship patterns across them, thus allowing for the discovery of natural social network behavior laws. This project will identify objectively measurable, macroscopic metrics pertaining to social network phenomena and explore under what conditions the computer-enabled empirical approach to knowledge discovery can be successful. The scientific inquiry approach based on the computer-enabled empirical learning paradigm will rely on recent advances in stochastic processes, systems theory, symbolic regression, evolutionary algorithms, and data fusion. Using standard social network metrics, computational experiments will be conducted and their results manually traversed in search for robust expressions (explicit formulae and invariants) corresponding to hidden symbolic laws. This project will benefit organizations and institutions that rely on fundamental understanding of social structures. It will further advances in communications, economics, engineering, psychology, sociology, epidemiology, and other domains engaged in studies of social interaction and behavior. On a broader level, this project will enrich the methodological toolbox of inquiry for other unexplored areas of modern science. Through student involvement activities and outreach, this research effort will enhance the educational environment supported by NSF and fostered at the University at Buffalo.

几个世纪以来，科学定律的发现一直是由对自然现象的实证研究推动的。然而，人类发现关系的能力在多维空间中变得紧张，这特别可以解释为什么我们没有看到任何基本法则来假设社会网络等复杂结构的结构或行为。该项目将利用基于符号回归的方法工具箱，目的是帮助人类大脑同时搜索多个社会网络数据集并检测它们之间的稳定关系模式，从而允许发现自然的社会网络行为规律。该项目将确定与社会网络现象相关的客观可测量的宏观指标，并探索在什么条件下计算机支持的知识发现经验方法可以成功。基于计算机支持的经验学习范式的科学探究方法将依赖于随机过程、系统理论、符号回归、进化算法和数据融合的最新进展。使用标准的社会网络指标，将进行计算实验，并手动遍历其结果，以寻找与隐藏符号定律对应的鲁棒表达式（显式公式和不变量）。这个项目将使那些依赖于对社会结构的基本理解的组织和机构受益。它将进一步推动传播学、经济学、工程学、心理学、社会学、流行病学和其他从事社会互动和行为研究的领域的发展。在更广泛的层面上，这个项目将丰富探索现代科学其他未探索领域的方法论工具箱。通过学生参与活动和外展，这项研究工作将改善由美国国家科学基金会支持并在布法罗大学培养的教育环境。