NeTS: Small: RUI: Dynamic Mathematical Modeling Towards Understanding Information Diffusion in Online Social Networks

NeTS：小：RUI：理解在线社交网络中信息扩散的动态数学模型

• 批准号: 1218212
• 负责人: Feng Wang
• 金额: $ 25万
• 依托单位: Arizona State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-09-01 至 2016-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1218212&HistoricalAwards=false
• 关键词: NeTS; Small; RUI; Dynamic; Mathematical

Online social networks have played an increasingly important role in connecting people and spreading information. It becomes urgent and necessary to understand the information diffusion process over large scale online social networks. However, due to the intricacy of human dynamics and social interactions, the vast scale of users and information, and the heterogeneity and diversity of online social networks, understanding information diffusion in social networks remains a daunting task. The PIs explore one key question in this project: how does a piece of information travel over time and space in an online social network. Similar questions have been asked and investigated for the outbreak of epidemics, but little attempt has been made in online social networks to model the diffusion of information from both temporal and spatial dimensions due to the challenges in quantitatively modeling the diffusion process in online social networks. The PIs combine the advances in data mining, graph theory, and dynamic mathematical modeling, specifically partial differential equation modeling, to characterize and predict the temporal and spatial dynamics of information diffusion. Broader impact: This work not only builds the first extendable theoretical framework for characterizing, modeling and predicting the diffusion process of information with dynamic mathematical modeling approach, but also helps facilitate the spreading of positive information and limit the distribution of unwanted misinformation such as spam and phishing messages because it can gain an in-depth understanding of information diffusion in social networks. Moreover, this project provides an opportunity for undergraduate students to gain valuable research experiences in mathematical modeling, graph theory and large scale data collection and analysis, and to prepare them for the challenges and opportunities in the increasingly important field of online social networks.

在线社交网络在连接人员和传播信息方面发挥了越来越重要的作用。在大规模的在线社交网络上了解信息传播过程是迫切和必要的。但是，由于人类动态和社交互动的复杂性，用户和信息的大量规模以及在线社交网络的异质性和多样性，了解社交网络中信息的扩散仍然是一项艰巨的任务。 PI探讨了这个项目中的一个关键问题：一块信息如何在在线社交网络中随时间和空间传播。已经提出和调查了类似的问题，以解决流行病的爆发，但是在线社交网络中几乎没有尝试过建模信息从时间和空间维度的扩散，这是由于定量建模在线社交网络中扩散过程的挑战。 PI结合了数据挖掘，图理论和动态数学建模（特别是部分微分方程模型）的进步，以表征和预测信息扩散的时间和空间动力学。更广泛的影响：这项工作不仅建立了第一个可扩展的理论框架，用于通过动态的数学建模方法来表征，建模和预测信息的扩散过程，而且还有助于促进积极信息的传播并限制不需要的错误信息的分布，例如垃圾邮件和网络播种信息，因为它可以获得网络中信息扩散的信息，因为它可以获得网络中信息扩散的信息。此外，该项目为本科生提供了一个机会，可以在数学建模，图理论和大规模数据收集和分析方面获得宝贵的研究经验，并为它们做好准备，以应对日益重要的在线社交网络领域的挑战和机遇做好准备。