AitF: FULL: Collaborative Research: Modeling and Understanding Complex Influence in Social Networks

AitF：完整：协作研究：建模和理解社交网络中的复杂影响

• 批准号: 1535900
• 负责人: Jie Gao
• 金额: $ 35.68万
• 依托单位: SUNY at Stony Brook
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-01 至 2019-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1535900&HistoricalAwards=false
• 关键词: AitF; FULL; Collaborative; Research; Modeling

Information, beliefs, diseases, technologies, and behaviors propagate through social interactions as a contagion. Understanding of how these contagions spread is crucial in encouraging beneficial and healthy behaviors and discouraging the ones that are destructive and damaging. Rigorous, mathematical understanding of complex social contagions is not just an abstraction, but will guide applications from healthcare to word-of-mouth advertising. The technical content of this project is inherently interdisciplinary, and its lessons will apply to related fields such as probability, economics, sociology, and statistical physics. The research efforts are integrated with the educational and outreach activities of the PIs, who have strong records of broadly disseminating cutting-edge research to high school, undergraduate, and graduate students through teaching, outreach programs, and personal mentoring. This project will transform our understanding of social contagions by: 1) Developing a suite of technical tools to enable improved understanding of specific complex processes; 2) Determining how various parameters of cascade and social structure together impact the chances of a cascade's success or failure; and 3) Obtaining empirical evidence to both corroborate the theoretical findings, and uncover the space of realistic setting for certain parameters. Many existing models of contagion assume that increasing the number of infected (or affected) neighbors marginally decreases the chance of infection. Many contagions, such as adoption of expensive new technology, fail to have this property, but instead have more complex rules for infection. This leads to different spreading behaviors even on the same networks. Motivated by sociology research findings, this project will greatly enhance our understanding of social contagions in three aspects. First this project will provide rigorous study of the spreading behavior of a simplified theoretical model called k-complex contagions and its interactions with structures in the underlying graph such as tie strength, unusually influential nodes, and community structures. Second, this project presents a general model for studying cascades that is both theoretically tractable and practically motivated. The general model generalizes most previous theoretical models of complex and simple contagions and includes homophily and environmental factors on cascades. Finally, this project will use post-hoc analysis as well as real world social experiments to verify the veracity of the model and fit the parameters in different settings.

信息、信仰、疾病、技术和行为以传染病的形式通过社会互动传播。了解这些传染是如何传播的，对于鼓励有益和健康的行为，阻止破坏性和破坏性的行为至关重要。对复杂的社会传染进行严格的数学理解不仅仅是一种抽象，而且将指导从医疗保健到口碑广告的应用。本项目的技术内容本质上是跨学科的，其经验教训将适用于相关领域，如概率论、经济学、社会学和统计物理学。研究工作与pi的教育和推广活动相结合，pi在通过教学、推广项目和个人指导向高中、本科生和研究生广泛传播前沿研究方面有着良好的记录。该项目将通过以下方式改变我们对社会传染的理解：1)开发一套技术工具，以提高对特定复杂过程的理解；2)确定级联的各种参数和社会结构如何共同影响级联成功或失败的机会；3)获得经验证据，既可以证实理论发现，又可以揭示某些参数的现实设置空间。许多现有的传染模型假设，增加受感染（或受影响）邻居的数量，会略微降低感染的机会。许多传染病，例如采用昂贵的新技术，没有这种特性，而是有更复杂的感染规则。这就导致了即使在相同的网络中，传播行为也是不同的。在社会学研究成果的推动下，本项目将从三个方面大大增强我们对社会传染的理解。首先，该项目将对一个被称为k-复传染的简化理论模型的传播行为及其与基础图中的结构（如纽带强度、异常影响节点和社区结构）的相互作用进行严格研究。其次，该项目提出了一个研究级联的一般模型，该模型在理论上易于处理，并且具有实践动机。一般模型概括了大多数先前的复杂和简单传染的理论模型，并包括级联上的同质性和环境因素。最后，本项目将使用事后分析以及现实世界的社会实验来验证模型的准确性，并在不同的设置中拟合参数。