BIGDATA: Collaborative Research: IA: Novel Bootstrap Procedures for Efficient Large Social Network Analysis

了解社交网络的结构和动态对于检测任何异常行为和管理其影响至关重要。大多数现有方法将网络视为一系列快照，其中快照表示给定时间段内网络的状态。因此，需要在每个快照上单独执行不同的网络操作。实际上，在线社交网络在不断发展，因此，网络操作应该随着网络的发展而自动执行，并且需要高效且可靠地完成。从这个角度来看问题，使我们能够创建一个支持高级、真实用例的解决方案，例如跟踪给定节点的邻域或跟踪网络连接如何及时发展，以确定有效的营销活动。这些例子表明，随着大型网络的发展，需要有效的计算技术，重要的网络统计。为了解决这个问题，该项目的研究人员利用自举和其他基于统计重新采样的方法补充了现有的分布式不断发展的社交图分析技术。最终目标是开发新的数据驱动工具，以便在需要时，不仅可以有效地计算统计网络模型的某些估计值，而且可以可靠地量化其估计误差。该项目主要针对大型稀疏网络上的异常和离群值检测开发新的高效和鲁棒的方法。由此产生的方法提供了以下功能：1）一个计算高效的有限样本推理广泛的网络拓扑统计; 2）一个灵活的数据驱动的表征网络结构和动态，以及3）全面量化建模和估计大型网络的不确定性，而不强加限制性条件的网络模型规范。预期的进展是在研究方法-数据驱动的大型稀疏网络的非参数推理的新方法，并在数字通信时代的网络动态和形成的知识的大幅提高。该项目可以通过提供对大型网络的跨学科应用的广泛接触和培养对跨学科关系的认识，从而提高他们的批判性思维能力和开辟新的职业道路，使学生受益匪浅。

Understanding the structure and dynamics of social networks is crucial for detecting any anomalous behavior and for managing its impacts. Most existing approaches view a network as a series of snapshots, where a snapshot represents the state of a network in a given time period. Therefore, different network operations need to be individually performed over each snapshot. In reality, online social networks are continuously evolving and therefore, network operations should be automatically performed as networks evolve and need to be done efficiently and reliably. Viewing the problem from this perspective allows us to create a solution that supports advanced, real-world use cases such as tracking the neighborhood of a given node or tracking how network connections evolve in time to determine effective marketing campaigns. These examples indicate the need for efficient computing techniques for important network statistics as the large networks evolve over time. To address this problem, the researchers in this project complement existing distributed evolving social graph analysis techniques with bootstrap and other statistical re-sampling based approaches. The ultimate goal is to develop novel data-driven tools so that when needed, not only certain estimates of statistical network models could be computed efficiently but their estimation errors are reliably quantified. This project primarily targets development of new efficient and robust methods for anomaly and outlier detection on large sparse networks. The resulting methodology provides the following functions: 1) a computationally efficient finite sample inference for an extensive range of network topology statistics; 2) a flexible data-driven characterization of network structure and dynamics, and 3) comprehensively quantifying uncertainty in modeling and estimation of large networks, without imposing restrictive conditions on network model specification. The expected advances are both in research methods - new approaches to data-driven nonparametric inference for large sparse networks and in substantial enhancement of knowledge of network dynamics and formation in the era of digital communication. The project can significantly benefit students by providing a broad exposure to interdisciplinary applications of large network and fostering awareness of interdisciplinary relationships -- hence enhancing their capacity for critical thinking and opening up new career paths.