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

几个世纪以来，科学定律的发现一直是由对自然现象的经验调查推动的。然而，人类发现关系的能力在多维空间中变得紧张，这个项目将利用基于符号回归的方法工具箱，帮助人类大脑同时搜索多个社会网络数据集，并检测稳定的社会社交网络行为。它们之间的关系模式，从而允许发现自然的社会网络行为规律。这个项目将确定客观可测量的，宏观的指标有关的社会网络现象，并探讨在什么条件下，计算机支持的经验方法，知识发现可以成功。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。使用标准的社交网络指标，将进行计算实验，并手动遍历其结果，以寻找与隐藏的符号法则相对应的鲁棒表达式（显式公式和不变量）。该项目将使依赖于对社会结构的基本理解的组织和机构受益。它将进一步促进通信，经济学，工程学，心理学，社会学，流行病学和其他领域从事社会互动和行为的研究。 在更广泛的层面上，该项目将丰富现代科学其他未探索领域的研究方法工具箱。通过学生参与活动和推广，这项研究工作将加强由NSF支持的教育环境，并在布法罗大学培养。

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.