DHB: Large-scale Spatially Embedded Interpersonal Networks: Measurement, Modeling, and Dynamics

DHB：大规模空间嵌入式人际网络：测量、建模和动力学

• 批准号: 0827027
• 负责人: Carter Butts
• 金额: $ 74.92万
• 依托单位: University of California-Irvine
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-10-01 至 2014-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0827027&HistoricalAwards=false
• 关键词: DHB; Large; scale; Spatially; Embedded

The past several decades have seen a tremendous growth in the depth and breadth of our knowledge regarding social networks. While the majority of research to date has centered on the study of networks within small communities, the properties of networks in larger populations constitute a topic of increasing interest. Prior research has pointed to geography as a key factor in network formation, suggesting a spatial approach to the description and modeling of large-scale network structure. Further progress toward understanding the dynamics of large-scale social networks is hampered by the lack of data on the geographical structure of personal ties, and by the relative paucity of specialized methods for analyzing such data. The purpose of this project is to address these issues through two basic thrust areas: the collection of novel data on the spatial structure of large-scale social networks, and methodological research that will allow these data to contribute to our scientific understanding of social structure. The data to be collected will contain information about the spatial and demographic structure of personal ties at regional and national scales. These data will enable the estimation and evaluation of models for the effects of geography on network structure, and for the impact of social networks on neighborhood-level outcomes. The methodological research conducted as part of this project will enhance current network models -- which characterize the probability of different types of social ties between individuals -- by including the effects of spatial constraints, geographic heterogeneity, and demography. These data and methodological contributions will enhance fundamental knowledge about the complex ways that individuals interact with other through space. To perform these tasks, this project brings together an interdisciplinary team drawn from the fields of sociology, geography, and criminology; the team's research will leverage current developments in network modeling and spatial statistics, as well as computational advances in the simulation of networks with large numbers of nodes. In addition to expanding our knowledge of the social ties which help knit communities together, the project will create new data and software tools that will be shared with other researchers and educators. The practical utility of the methodology and tools developed through this project will also be demonstrated with an application of spatial network modeling to the assessment and calibration of information dissemination systems for use during natural disasters and other large-scale emergency situations. By measuring and modeling the properties of large-scale social networks, this project will yield new insights into the social fabric in which people are all embedded.

在过去的几十年里，我们对社交网络的了解在深度和广度上都有了巨大的增长。虽然到目前为止，大多数研究都集中在研究小社区内的网络，但较大人口中网络的特性构成了一个越来越感兴趣的话题。先前的研究已经指出，地理是网络形成的关键因素，建议使用空间方法来描述和建模大规模网络结构。由于缺乏关于人际关系地理结构的数据，以及相对缺乏分析这类数据的专门方法，进一步了解大规模社交网络动态的进展受到了阻碍。这个项目的目的是通过两个基本的主要领域来解决这些问题：收集关于大规模社会网络空间结构的新数据，以及进行方法研究，使这些数据有助于我们对社会结构的科学理解。要收集的数据将包含关于区域和国家两级个人关系的空间和人口结构的信息。这些数据将有助于估计和评估地理对网络结构的影响以及社会网络对邻里层面结果的影响的模型。作为该项目的一部分进行的方法论研究将通过包括空间限制、地理异质性和人口统计学的影响来增强当前的网络模型--该模型表征了个人之间不同类型的社会联系的可能性。这些数据和方法的贡献将加强关于个人通过空间与他人互动的复杂方式的基本知识。为了执行这些任务，该项目汇集了一个来自社会学、地理学和犯罪学领域的跨学科团队；该团队的研究将利用网络建模和空间统计方面的最新发展，以及在模拟具有大量节点的网络方面的计算进展。除了扩大我们对有助于将社区联系在一起的社会关系的了解外，该项目还将创建新的数据和软件工具，并将与其他研究人员和教育工作者共享。通过该项目开发的方法和工具的实际效用也将通过空间网络建模应用于评估和校准在自然灾害和其他大规模紧急情况下使用的信息传播系统。通过测量和建模大规模社交网络的属性，该项目将对所有人都嵌入其中的社交结构产生新的见解。