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

NeTS：媒介：协作研究：为在线社交活动构建时间胶囊

• 批准号: 1302197
• 负责人: Jun Xu
• 金额: $ 30.4万
• 依托单位: Georgia Tech Research Corporation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-10-01 至 2017-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1302197&HistoricalAwards=false
• 关键词: NeTS; Medium; Collaborative; Research; Towards

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.

维护，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际含义，例如，用于营销，建议过滤和研究社会行为。正在收集有关用户在线社交活动的大量经验数据集，但它们通常太大而无法分析。该项目的目的是设计图形生成模型和摘要技术，这些模型可以保留有关在线社交互动的相关信息，这些信息导致有趣的事件，例如，信息的病毒扩散或对用户行为的急剧变化。为此，该项目将开发能够捕获用户活动图（UAG）动态演变的生成模型，该模型代表了一系列用户通信/动作。在微观层面，该项目将研究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，这些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，例如识别信息cascades的“有影响力”的源头。 （例如，经济学，历史，政治科学和社会科学）。该项目将通过课堂课程开发和在线教学来帮助培训在线社交网络和网络科学领域的未来研究人员和从业人员。 PI将继续持续招募多样性，并向K-12学生推广。通过与行业的伙伴关系，将技术转移到商业实践中是可行的。