NetSE: Small: Structure and Dynamics of Complex Networks

NetSE：小型：复杂网络的结构和动态

• 批准号: 0915678
• 负责人: Kristina Lerman
• 金额: $ 47.63万
• 依托单位: University of Southern California
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-01 至 2013-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0915678&HistoricalAwards=false
• 关键词: NetSE; Small; Structure; Dynamics; Complex

The Social Web, or Web 2.0, has changed the way people connect with each other and use information. Sites such as Twitter, Flickr, and Digg allow people to create content, annotate it with descriptive labels, and befriend others to create communities. The collective knowledge and expertise of the community is expressed through the links between people and information. The key to extracting this knowledge is understanding the structure and dynamics of networks.This project will study dynamics of information spread on networks and how it relates to network structure and quality of information. In previous work, investigator has developed a mathematical framework to study the properties of static networks. She showed that a centrality metric based on the number of paths connecting nodes can be used to identify groups and important nodes within them. However, looking at static structure ignores valuable temporal information that can be used to improve the ability to identify influential nodes and hidden groups, as well as quickly and reliably predict important trends and evaluate the quality of information. The investigator will extend these metrics to dynamic networks and model information spread on networks. The investigator will apply these methods to complex networks that link different types of entities, namely, people, content, and groups.Considering network structure in the dynamics of information spread will lead to more effective tools to leverage community's knowledge to address a number of problems, including identifying important trends, assessing information quality, and separating true information from rumors.

社交网络，或Web 2.0，改变了人们相互联系和使用信息的方式。像Twitter、Flickr和Digg这样的网站允许人们创建内容，用描述性标签注释内容，并与其他人交朋友以创建社区。社区的集体知识和专门知识通过人与信息之间的联系来表达。获取这些知识的关键是理解网络的结构和动态。本项目将研究网络上信息传播的动态以及它与网络结构和信息质量的关系。在以前的工作中，研究者已经建立了一个数学框架来研究静态网络的性质。她表明，基于连接节点的路径数量的中心性度量可以用于识别其中的组和重要节点。然而，静态结构忽略了有价值的时间信息，这些信息可用于提高识别有影响力的节点和隐藏组的能力，以及快速可靠地预测重要趋势和评估信息质量。研究人员将这些指标扩展到动态网络和网络上传播的信息模型。研究人员将把这些方法应用于连接不同类型实体的复杂网络，即人、内容和群体。在信息传播的动态中考虑网络结构将产生更有效的工具，以利用社区的知识来解决一些问题，包括识别重要趋势，评估信息质量，并将真实信息与谣言分开。