III: Small: Analysis and Models of Social Network Structure, Growth and Dynamics

III：小：社交网络结构、增长和动态的分析和模型

• 批准号: 1321083
• 负责人: Ben Zhao
• 金额: $ 50万
• 依托单位: University of California-Santa Barbara
• 依托单位国家: 美国
• 项目类别: Continuing 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=1321083&HistoricalAwards=false
• 关键词: III; Small; Analysis; Models; Social

Online social networks (OSNs) such as Facebook and LinkedIn are valuable infrastructures for communication and interactions between a large volume of Internet users. For years, researchers have been trying to answer fundamental questions about the formation of these complex networks, their ongoing evolution, formation of internal structures, and change at different time scales. Since answering these questions requires real dynamics datasets at scale, most prior studies have been significantly constrained by a lack of data. The Principal Investigators have been granted access by an OSN provider to a uniquely detailed and complete trace of dynamics over 2+ years of a social network. The goal is to mine and analyze the traces of network dynamics to validate existing models and guide new models for fine grain network dynamics. Objectives include analysis of the preferential attachment model at different stages of network growth, developing new models of network dynamics at fine granularity in both time and graph topology, and explorations of applications driven by novel metrics of graph dynamics.The work has the potential to dramatically change our understanding of dynamics in online social networks. By taking an empirical, data-driven approach to network modeling, they can shed light on how traditional models of network dynamics deviate from ground truth. In addition, they are developing empirical models that are more effective at accurately predicting network events at small scales. Both PIs Zhao and Zheng are heavily invested in educational and outreach programs for female and minority students: female students and postdocs often outnumber male counterparts in their lab. The PIs will disseminate their results to their collaborators atRenren and LinkedIn, and also share results with researchers at Twitter, Zynga, Facebook and Google through existing technical contacts and informal visits/talks. For further information, please see the project webpage (http://sandlab.cs.uchicago.edu/dynamics/).

Facebook 和 LinkedIn 等在线社交网络 (OSN) 是大量互联网用户之间沟通和互动的宝贵基础设施。多年来，研究人员一直试图回答有关这些复杂网络的形成、其持续演化、内部结构的形成以及不同时间尺度变化的基本问题。由于回答这些问题需要大规模的真实动态数据集，因此大多数先前的研究都受到数据缺乏的严重限制。首席研究员已获得 OSN 提供商的授权，可以访问社交网络 2 年多以来独特详细且完整的动态跟踪。目标是挖掘和分析网络动态的痕迹，以验证现有模型并指导细粒度网络动态的新模型。目标包括分析网络增长不同阶段的优先连接模型，在时间和图拓扑的细粒度上开发新的网络动态模型，以及探索由新的图动态指标驱动的应用程序。这项工作有潜力显着改变我们对在线社交网络动态的理解。通过采用实证、数据驱动的网络建模方法，他们可以揭示传统的网络动态模型如何偏离真实情况。此外，他们正在开发能够更有效地准确预测小规模网络事件的经验模型。 PI 赵和郑都在针对女性和少数族裔学生的教育和推广项目上投入了大量资金：实验室中女学生和博士后的数量往往超过男性同行。 PI 将向人人网和 LinkedIn 的合作者传播他们的研究结果，并通过现有的技术联系和非正式访问/会谈与 Twitter、Zynga、Facebook 和 Google 的研究人员分享结果。有关更多信息，请参阅项目网页 (http://sandlab.cs.uchicago.edu/dynamics/)。