Compact Bayesian Models of Massive Social Graphs

本研究项目将开发和应用新的统计模型，捕捉动态，大规模社交网络的局部结构。 随着数字技术在世界各地的普及，社会科学研究人员越来越多地获得反映人类互动和社会行为的颗粒和细微差别的数据。 这是对传统社交网络研究的一个改变，传统研究需要昂贵而耗时的调查模块。 考虑到从调查中收集网络数据的成本，有关个人社交网络在行为中的作用的大多数知识都来自于在单个时间段观察到的相对较小的网络的数据。 相比之下，研究人员将他们的方法应用于未经请求的数据，这些数据是通过社交媒体和通信服务生成的电子日志。 在这种情况下，互动发生在真实的时间，涉及数百万独特的个人。 研究这些数据可以深入了解塑造个人社会环境的日常互动模式。 将开发的技术将以直观和简化的表示方式模拟丰富的网络结构，使其更容易融入社会科学研究。 开源软件将通过CRAN和Python包存储库提供。 通过ICPSR或GitHub可以获得复制已发表文章结果所需的聚合数据和相关材料。该项目将开发一个贝叶斯框架，该框架使用一种新的稀疏编码方法来简约地表示非常大的图的子结构。 该项目还将提供一套直观的方法，旨在使社会科学家能够更好地了解新形式的海量社交网络数据的潜在结构。 尽管有主动提供的数据源的承诺，但数据的大小和粒度带来了巨大的挑战。 从统计学的角度来看，数据的巨大规模和异质性使现有的计算工具感到困惑。 从社会科学的角度来看，这些数据代表了更深层次的社会关系的表现。 该项目弥合了这些大型社会网络图的数学/统计描述与底层社会结构之间的差距。 局部网络结构的贝叶斯表示将建立在网络模体和随机图模型的相关研究基础上，从而能够对现实世界的网络过程进行有意义的推断。 稀疏编码方法将允许非常大的组的紧凑和可扩展的表示。 该项目将有助于在社交网络，贝叶斯模型的不确定性和网络推理的贝叶斯模型的相关统计文献。

This research project will develop and apply new statistical models that capture dynamic, local structure in large-scale social networks. As digital technologies proliferate around the world, social science researchers increasingly have access to data that reflect granular and nuanced patterns of human interactions and social behavior. This is a change from traditional research on social networks, which requires expensive and time-consuming survey modules. Given the cost of collecting network data from surveys, most knowledge about the role of individuals' social networks in behavior comes from data about relatively small networks observed at a single time period. In contrast, the investigators will apply their methods to data that are unsolicited and arise as electronic logs generated through social media and communication services. In such instances, interactions occur in real time and involve millions of unique individuals. Examining such data provides insights into the patterns of day-to-day interactions that shape individuals' social context. The techniques to be developed will model rich network structure with intuitive and simplified representations that will make them easier to integrate into social science research. Open-source software will be made available through CRAN and the Python Package Repository. Aggregated data and related materials required to replicate results of published articles will be available thru ICPSR or GitHub.This project will develop a Bayesian framework that parsimoniously represent the sub-structures of very large graphs with a novel approach to sparse coding. The project also will provide an intuitive set of methods that are designed to enable social scientists to better understand the latent structure in new forms of massive social network data. Despite the promise of unsolicited data sources, the size and granularity of the data present substantial challenges. From a statistical perspective, the massive size and heterogeneity of the data confound existing computational tools. From a social science perspective, the data represent the manifestations of deeper social relationships. This project bridges the gap between the mathematical/statistical descriptions of these large social network graphs and the underlying social structure. The Bayesian representation of local network structure will build upon related research on network motifs and random graph models, enabling meaningful inference about real-world network processes. The sparse coding approach will allow for the compact and scalable representation of extremely large groups. The project will contribute to related statistical literatures in Bayesian models for social networks, Bayesian model uncertainty, and network inference.