III: Small: Inferring first movers in large-scale socio-technical networks

III：小型：推断大规模社会技术网络中的先行者

• 批准号: 1219071
• 负责人: Vijay Subramanian
• 金额: $ 50万
• 依托单位: Northwestern University
• 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 2015-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1219071&HistoricalAwards=false
• 关键词: III; Small; Inferring; first; movers

This project is studying statistical inference from datasets tracking the diffusion of new ideas or behaviors through a population. Game theoretic models for the diffusion are utilized in which members of the population decide to adopt a technology by maximizing a pay-off that depends on an underlying network structure. Example questions include the identification of "first movers" and the most likely series of actions that result in a given observed state of the network. Algorithms are being developed for characterizing the maximum likelihood estimate of first movers for an evolutionary game theoretic framework with smoothed best response dynamics. Additionally algorithms to identify influential nodes and the network graph along with the associate payoff functions are being studied. The associated modeling and analysis build upon foundations in probability and statistics, Markov processes, statistical mechanics, optimization and game theory.Understanding diffusions in social networks is broadly applicable across society including areas such as marketing, economics and social sciences; efforts are being made to disseminate the results of this work to such fields as well as to incorporate ideas into undergraduate and graduate courses in EECS.

该项目正在研究数据集的统计推断，跟踪新想法或行为在人群中的传播。利用扩散的博弈论模型，其中人口成员决定通过最大化取决于底层网络结构的回报来采用某项技术。示例问题包括识别“先行者”以及导致给定观察到的网络状态的最有可能的一系列操作。正在开发算法来表征具有平滑最佳响应动态的进化博弈论框架的先行者的最大似然估计。此外，正在研究识别有影响力的节点和网络图以及相关收益函数的算法。相关的建模和分析建立在概率和统计学、马尔可夫过程、统计力学、优化和博弈论的基础上。理解社交网络中的扩散广泛适用于整个社会，包括市场营销、经济学和社会科学等领域；我们正在努力将这项工作的成果传播到这些领域，并将想法纳入 EECS 的本科生和研究生课程中。