Statistical Models for Three-Way Social Network Dynamics

本研究计画将开发社交网络的动态模型，其中关系涉及三个社会实体。传统的社交网络数据模型关注的是两方（发送者-接收者）关系。用额外的维度补充这些模型对于理解从网络感知偏差（感知者-发送者-接收者关系）到流言（发送者-接收者-目标关系）的社会现象至关重要。所开发的方法将适用于许多实质性领域，包括教育学、政治学、心理学和社会学。对儿童和青少年之间的八卦动态及其对健康和业绩结果的后果进行大规模研究将构成该项目的主要应用之一。通过已建立的合作关系，该项目将直接影响政策，并帮助制定成功的社会排斥预防战略。开发可公开获得的软件、编制培训材料和举办跨学科讲习班，将确保广泛利用新的模式。社会科学和统计学的研究生将接受培训和指导，作为研究过程的一部分。本研究成果将纳入本科课程设置，并在随机行动者导向的建模框架下建立三路网络数据动态模型。这些连续时间模型将使研究人员能够研究个人的网络决策和控制网络变化的微观社会机制。项目结果将包括两个时间戳的关系事件数据和纵向网络面板数据的估计程序。为了解释个体的异质性，将建立一个用于研究三向网络动态的随机效应模型。该项目将利用新开发的方法，探讨人们感兴趣的实质性问题和方法问题。例如，假设个人的关系行为基于对“真实”网络的共同认知，这是社交网络模型的标准。与此同时，这是值得怀疑的，因为人们的网络感知是不同的。使用新方法的模拟研究将评估这一假设的影响。该奖项反映了NSF的法定使命，并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。

This research project will develop models for the dynamics of social networks in which relationships involve three social entities. Conventional models for social network data focus on two-party (sender-receiver) relationships. Complementing these models with an extra dimension is critical to understanding social phenomena ranging from network perception biases (perceiver-sender-receiver relations) to gossip (sender-receiver-target relations). The methods to be developed will be applicable to many substantive fields, including education, political science, psychology, and sociology. A large-scale study on the dynamics of gossip among children and adolescents and its consequences on health and performance outcomes will constitute one of the major applications of this project. Through established collaborations, the project will directly influence policy and help to construct successful prevention strategies for social exclusion. The development of publicly available software and the creation of training materials and cross-disciplinary workshops will ensure broad access to the new models. Graduate students in the social sciences and statistics will be trained and mentored as part of the research process. The results of this research will be incorporated into the undergraduate course setting.The models for the dynamics of three-way network data will be formulated in the stochastic actor-oriented modeling framework. These continuous-time models will enable researchers to study individuals' network decisions and the micro-level social mechanisms governing network change. Project results will include estimation procedures for both time-stamped relational event data and for longitudinal network panel data. To account for individual heterogeneity, a random effects model for the study of three-way network dynamics will be developed. Using the newly developed methods, the project will explore both substantive and methodological questions of interest. For example, the assumption that individuals base their relational behavior on a shared cognition of a 'true' network is standard in social network models. At the same time, it is questionable since people's network perceptions are known to differ. Simulation studies using the new methods will evaluate the impact of this assumption.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.