CIF: Small: Modeling and Dynamic Analyzing for Multiplex Social Networks

CIF：小型：多重社交网络的建模和动态分析

• 批准号: 1422116
• 负责人: My Thai
• 金额: $ 26.9万
• 依托单位: University of Florida
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-09-01 至 2018-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1422116&HistoricalAwards=false
• 关键词: CIF; Small; Modeling; Dynamic; Analyzing

The rapid growth of online social networks has made them become one of the most important channels for fast information propagation and influence. This propagation process becomes much more effective when information simultaneously spread on many social sites via overlapping users, who have accounts on multiples social networks sites. Analyzing each network separately will fail to identify the most influential users, and thus give erroneous indicators of information propagation. At present, understanding behavior that crosses multiple networks with multiple layers, dubbed here Multiplex Social Networks, is still largely unexplored. This project aims to develop mathematical models and techniques to precisely and efficiently analyze the information propagation in multiplex networks, capturing the extrinsic interdependencies between networks while preserving each network?s intrinsic properties.At the heart of this project lie novel mathematical techniques to study many dynamical processes in multi-layered multi-dependent networks. This research will lay a foundation in understanding the fundamental properties that contribute to an extremely fast propagation in multiplex social networks, providing a breakthrough in effectively using online social networks and their crowd sourcing capabilities. Furthermore, algorithmic techniques developed in this project are expected to advance research fronts in approximation theory and network science. The findings may also benefit other fields, such as network immunization, offering control of epidemic outbreaks and containment of viruses, and likewise allow us to analyze other dynamic features, such as cascading failures in many modern complex networked systems, which are interdependent. The project will involve students at all levels, with emphasis on attracting students from underrepresented groups via an internship program. The real-world applications will offer an ideal platform to engage undergraduate and K-12 students.This project will comprehensively investigate propagation behaviors in multiplex social networks by pursuing three primary research tasks: 1) modeling a multiplex social network via a new set of graph theory techniques. The model provides a unified framework to extract the interdependencies between networks and the properties of each network in the simplest form; 2) Analyzing the speed of information propagation and identifying the most influential users in the presence of simultaneous spreading via overlapping users. Several new optimization models along with their hardness complexity analyses and approximation algorithms will be investigated; 3) Devising near-optimal solutions for many optimization problems considering the properties of complex networks since many real-world networks, ranging from the Internet to social and biological networks, are complex. This is a novel research direction and success of this approach will impact the design of approximation algorithms for many real-life problems on complex networks.

在线社交网络的快速增长使它们成为快速信息传播和影响力最重要的渠道之一。当信息同时通过重叠的用户在许多社交网站上有帐户的重叠用户在许多社交网站上传播时，这个传播过程就会变得更加有效。分别分析每个网络将无法确定最有影响力的用户，从而给出错误的信息传播指标。目前，了解在这里被称为多重社交网络的多层跨越多层网络的行为仍然在很大程度上没有探索。该项目旨在开发数学模型和技术，以精确有效地分析多重网络中的信息传播，从而捕获网络之间的外部相互依赖性，同时保留每个网络的内在属性。在此项目的核心中，该项目的核心是新的数学技术，以研究多层多依赖性依赖性依赖性多依赖性的许多动态过程。这项研究将奠定基础，以理解有助于在多重社交网络中非常快速传播的基本属性，从而有效地使用在线社交网络及其人群采购功能有效地取得了突破。此外，该项目中开发的算法技术有望在近似理论和网络科学方面提高研究方面。这些发现还可能受益于其他领域，例如网络免疫，可以控制流行病暴发和病毒的控制，同样允许我们分析其他动态特征，例如许多相互依存的现代复杂网络系统中的级联故障。该项目将涉及各个级别的学生，重点是通过实习计划吸引来自代表性不足小组的学生。现实世界中的应用程序将为吸引本科生和K-12学生提供理想的平台。该项目将通过执行三个主要研究任务来全面研究多元社交网络中的传播行为：1）通过新的图理论技术对多元社交网络进行建模。该模型提供了一个统一的框架，以最简单的形式提取网络与每个网络的属性之间的相互依赖性； 2）分析信息传播的速度并在通过重叠用户同时扩散的情况下识别最有影响力的用户。将研究几种新的优化模型及其硬度复杂性分析和近似算法。 3）考虑复杂网络的属性，为许多优化问题设计了近乎最佳的解决方案，因为许多现实世界网络（从互联网到社交和生物网络）都很复杂。这是一种新颖的研究方向，这种方法的成功将影响复杂网络上许多现实生活问题的近似算法的设计。