AI 中文总结
本文介绍图统计这一非欧几里得数据分析新兴学科，追溯其理论基础等，阐述通过整合多种理论及准动态非线性建模创造新统计思维范式，能为多领域带来变革，为大数据转化为知识提供框架。
AI 中文摘要
复杂数据的爆炸式增长催生了图统计这一数据科学中的全新学科。与主要在欧几里得空间内运作的传统统计不同，图统计面对现代数据自然组织成由复杂互连组成的动态网络这一现实。本文概述了图统计这一新兴学科，追溯其理论基础、方法创新和变革性应用。探讨了通过准动态非线性建模整合进化博弈论、生态位理论、拓扑数据分析和图论如何创造了一种能够分析非欧几里得数据的新统计思维范式。展示了图统计如何有望彻底改变从数量遗传学、系统生物学到材料科学和人工智能等领域，为将大数据转化为实用知识提供一个有原则的框架。
英文摘要
The explosive growth of complex data has catalyzed the emergence of graph statistics as a fundamentally new discipline in data science. Unlike traditional statistics, which operates primarily within the comfortable confines of Euclidean spaces, graph statistics confronts the reality that modern data naturally organize themselves as dynamic networks composed of complex interconnections. In this article, we present an overview of graph statistics as an emerging discipline, tracing its theoretical foundations, methodological innovations, and transformative applications. We examine how the integration of evolutionary game theory, ecological niche theory, topological data analysis, and graph theory through quasi-dynamic nonlinear modeling has created a new norm of statistical thinking capable of analyzing non-Euclidean data. We show how graph statistics is poised to revolutionize fields ranging from quantitative genetics and systems biology to materials science and artificial intelligence, offering a principled framework for transforming big data into practical knowledge.
Comments15 pages;1 figures
