NeTS: Medium: Collaborative Research: Towards Building Time Capsule for Online Social Activities

在线社交网络上保存、建模和预测信息级联的能力具有许多理论和实践意义，例如，用于市场营销、推荐过滤和社会行为研究。关于用户在线社交活动的大量经验数据集正在被收集，但它们往往太大而无法分析。这个项目的目标是设计图形生成模型和摘要技术，可以保存有关导致有趣事件的在线社交互动的相关信息，例如，信息的病毒式传播或用户行为的急剧变化。为此，该项目将开发生成模型，可以捕获用户活动图（UAGs）的动态演变，这代表了一系列用户间的通信/动作。在微观层面，该项目将调查用户对征聘过程的影响。此外，该项目还将设计图形摘要技术，在数据压缩率、计算复杂性和回答基本查询的准确性之间实现良好的权衡，例如识别“有影响力”的信息级联源。如果成功，该项目将提供存储/归档大量图形数据的有效方法，以支持对在线社交互动动态的纵向研究，这对多个学科具有潜在影响（例如，经济学、历史学、政治学和社会学）。该项目将通过课堂课程开发和在线教学，帮助培训在线社交网络和网络科学方面的未来研究人员和从业人员。PI将继续进行多样性招聘和推广到K-12学生。通过与工业界建立伙伴关系，使技术转让成为可能。

The capability to preserve, model, and predict information cascades over online social networks has many theoretical and practical implications, e.g., for marketing, recommendation filtering, and studying of societal behavior. Massive empirical data sets on users' online social activities are being collected, but they are often too big to analyze. The goal of this project is to design graph generative models and summarization techniques that can preserve pertinent information about online social interactions that lead to interesting events, e.g., viral diffusion of information or drastic change of user behavior. Towards this end, the project will develop generative models that can capture the dynamic evolution of user activity graphs (UAGs), which represent a sequence of inter-user communications/actions. At the microscopic level, the project will investigate user influence in the recruitment process. In addition, the project will design graph summarization techniques that can achieve good tradeoffs between data compression ratio, computational complexities, and accuracy in answering fundamental queries, such as identifying 'influential' sources of information cascades.Broader Impact: If successful, this project will provide efficient methods for storing/archiving massive graph data to support longitudinal study on the dynamics of online social interactions, which has potential impact on multiple disciplines (e.g., economics, history, political sciences, and social science). This project will help train future researchers and practitioners in online social networking and network science through classroom curriculum development and online teaching. The PIs will continue ongoing diversity recruitment and outreach to K-12 students. Transfer of technology into commercial practice is made feasible through partnership with industry.