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

信息、信仰、疾病、技术和行为作为一种传染病通过社会互动传播。了解这些传染病是如何传播的，对于鼓励有益和健康的行为以及阻止具有破坏性和破坏性的行为至关重要。对复杂的社交传播进行严格的数学理解不仅是一个抽象的概念，还将指导从医疗保健到口碑广告的各种应用。该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、经济学、社会学和统计物理。研究工作与私人投资机构的教育和推广活动相结合，他们有通过教学、推广计划和个人指导向高中生、本科生和研究生广泛传播尖端研究的良好记录。这个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，使我们能够更好地理解特定的复杂过程；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；以及3)获得经验证据，既证实理论发现，又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，都没有这种特性，而是有更复杂的感染规则。这导致即使在相同的网络上也会有不同的传播行为。在社会学研究成果的推动下，这个项目将在三个方面极大地增强我们对社会传染的理解。首先，这个项目将提供一个名为k-复合体传染的简化理论模型的传播行为的严格研究，以及它与底层图中的结构(如纽带强度、异常有影响力的节点和社区结构)的相互作用。其次，这个项目提出了一个研究叶栅的通用模型，它在理论上是容易处理的，而且在实践中也是有动机的。一般模型推广了以往大多数复杂传染和简单传染的理论模型，并考虑了同质性和环境因素对级联的影响。最后，本项目将使用事后分析以及真实世界的社会实验来验证模型的准确性，并在不同的设置下对参数进行拟合。

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.