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

信息、信仰、疾病、技术和行为通过社会互动传播，成为传染病。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破坏性和破坏性的行为至关重要。对复杂的社会传染的严格的数学理解不仅仅是一种抽象，而是将指导从医疗保健到口碑广告的应用。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具有通过教学，推广计划和个人指导向高中，本科和研究生广泛传播前沿研究的良好记录。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）确定级联和社会结构的各种参数如何共同影响级联成功或失败的机会;（3）获得实证证据，既证实了理论发现，又揭示了某些参数的现实设置空间。许多现有的传染模型假设，增加感染（或受影响）邻居的数量会略微降低感染的机会。许多传染病，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。 这导致即使在相同的网络上也有不同的传播行为。本研究以社会学的研究成果为基础，从三个方面加深我们对社会传染的理解。首先，这个项目将提供一个简化的理论模型称为k-复合传染及其与基础图中结构的相互作用，如领带强度，异常影响力节点和社区结构的传播行为的严格研究。第二，本项目提出了一个一般模型，研究级联，这是理论上易于处理和实际动机。一般模型概括了大多数以前的理论模型的复杂和简单的传染病，并包括同质性和环境因素的级联。最后，本计画将使用事后分析以及真实的社会实验来验证模型的准确性，并在不同的设定下拟合参数。

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.