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

保存、建模和预测在线社交网络上的信息级联的能力具有许多理论和实践意义，例如，用于营销、推荐过滤和社会行为研究。人们正在收集大量关于用户在线社交活动的经验数据集，但这些数据往往太大而无法分析。该项目的目标是设计图形生成模型和总结技术，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，该项目将开发能够捕捉用户活动图（uag）动态演变的生成模型，uag表示用户间通信/操作的序列。在微观层面，该项目将调查用户在招聘过程中的影响。此外，该项目将设计图形摘要技术，可以在数据压缩比、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”信息级联来源。更广泛的影响：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，以支持在线社会互动动态的纵向研究，这对多个学科（如经济学、历史学、政治学和社会科学）具有潜在的影响。该项目将通过课堂课程开发和在线教学，帮助培养未来在线社交网络和网络科学的研究人员和实践者。pi将继续进行多元化招聘，并向K-12学生提供服务。通过与工业界合作，使技术转化为商业实践成为可能。

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.