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

在线社交网络在联系人们和传播信息方面发挥着越来越重要的作用。了解大规模在线社交网络中信息的传播过程变得迫切而必要。然而，由于人类动态和社会互动的复杂性，用户和信息的巨大规模，以及在线社交网络的异质性和多样性，理解社交网络中的信息扩散仍然是一项艰巨的任务。pi在这个项目中探索了一个关键问题：一条信息是如何在在线社交网络中随时间和空间传播的。对于流行病的爆发也提出了类似的问题并进行了调查，但由于在线社交网络中的扩散过程在定量建模方面存在挑战，因此很少尝试在在线社交网络中从时间和空间两个维度对信息扩散进行建模。pi结合了数据挖掘、图论和动态数学建模（特别是偏微分方程建模）方面的进展，以表征和预测信息扩散的时空动态。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，用动态数学建模方法来描述、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，限制垃圾邮件和网络钓鱼等不需要的错误信息的传播，因为它可以深入了解社交网络中的信息扩散。此外，该项目为本科生提供了一个在数学建模、图论和大规模数据收集与分析方面获得宝贵研究经验的机会，并为他们在日益重要的在线社交网络领域面临的挑战和机遇做好准备。

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.