Statistical Models for Three-Way Social Network Dynamics

本研究项目将开发社会网络动态模型，其中关系涉及三个社会实体。社交网络数据的传统模型侧重于双方（发送方-接收方）关系。用一个额外的维度来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）到八卦（发送者-接收者-目标关系）等社会现象至关重要。所开发的方法将适用于许多实质性领域，包括教育、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成绩结果的影响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，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。开发可公开使用的软件和编制培训材料和跨学科讲习班将确保广泛使用新模式。作为研究过程的一部分，社会科学和统计学的研究生将接受培训和指导。本研究结果将纳入本科课程设置。在面向随机因素的建模框架中，将建立三方网络数据的动态模型。这些连续时间模型将使研究者能够研究个体的网络决策和控制网络变化的微观社会机制。项目结果将包括时间戳关系事件数据和纵向网络面板数据的估计程序。为了考虑个体的异质性，我们将建立一个用于研究三方网络动力学的随机效应模型。利用新开发的方法，该项目将探索令人感兴趣的实质性和方法论问题。例如，在社会网络模型中，个体将其关系行为建立在对“真实”网络的共同认知之上的假设是标准的。同时，这也是值得怀疑的，因为人们对网络的感知是不同的。使用新方法的模拟研究将评估这一假设的影响。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.