ITR: Study of Dynamically Evolving Social Groups in Communication Networks

ITR：通信网络中动态发展的社会群体的研究

• 批准号: 0324947
• 负责人: Mark Goldberg
• 金额: --
• 依托单位: Rensselaer Polytechnic Institute
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2003
• 资助国家: 美国
• 起止时间: 2003-09-15 至 2010-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0324947&HistoricalAwards=false
• 关键词: ITR; Study; Dynamically; Evolving; Social

This project will develop novel methods for the statistical analysis of the evolution of social groups acting within a large social network via a communications network, such as the Internet. The basis for the analysis is a set of probabilitistic laws (micro-laws) that govern the individual behavior of actors, which largely determines the macro-evolution of the social groups. The parameters of the macro-evolution can be measured and then used to determine, via reverse engineering, the parameters of the micro-laws that fit the observed evolution. Understanding the evolution of social communities will help determine resource allocation strategies for enhancing the functioning of these communities. The methods developed in this research can be used to locate, within a large social network, social groups that try to hide their inner communications. Such a system can be instrumental in preventing terrorists' attacks similar to those on September 11, 2001. The proposed research will also open up novel educational opportunities at the home university related to social network analysis.

该项目将开发新的方法，通过互联网等通信网络，对在大型社会网络中活动的社会群体的演变进行统计分析。分析的基础是一套支配行动者个人行为的概率法则(微观法则)，它在很大程度上决定了社会群体的宏观演变。可以测量宏观演化的参数，然后通过反向工程来确定符合观察到的演化的微观定律的参数。了解社会社区的演变将有助于确定加强这些社区运作的资源分配战略。这项研究中开发的方法可以用来在大型社交网络中定位试图隐藏其内部交流的社交团体。这样的系统有助于防止类似2001年9月11日恐怖分子的袭击。这项拟议的研究还将在与社交网络分析相关的本土大学开辟新的教育机会。