CHS: Small: The Ties that Relate Us: Modeling the Impact of Relationships on Social Contagion and Network Dynamics

本项目旨在通过将社会系统中人与人之间关系的本质信息系统地纳入网络结构中来研究社会传染，从而帮助建立通过内隐和外显关系理解社会过程的基础，从而促进我们对在线社会结构的理解。许多社会过程，从友谊发展到谣言传播和同伴影响，都发生在个体之间相互关联的互动和不同类型关系的复杂结构上。为了以一种易于处理的方式对这种结构进行建模，研究人员通常依赖于网络或图形，其中节点代表人，而边则编码了两个人以某种方式相互联系的想法。这些简单的社会关系表示使我们能够制定复杂的预测模型和算法，并使我们能够理解许多这些过程背后的机制，如社会传染和信息扩散。然而，连接个体的纽带是两个人之间潜在的丰富的、多方面的关系的标志，这些关系通常具有隐含的社会维度、启示和约束，远远超出了网络结构中直接编码的内容。认识到这些关系所涉及的所有复杂性将提高我们准确建模和预测网络上发生的许多社会动态的能力。 本研究分为三个主要阶段：（1）开发新的方法来检测和量化传染和关系之间的关联强度;（2）在边缘关系和主题能够相互作用的多元网络上开发信息扩散和社会传染的新模型，以实现更好的预测准确性，以及（3）放松静态网络的假设，以便开发新的方法来预测动态的、演进的社交网络中的关系类型和干扰频率的变化。现有的模型通常假设社会动态是独立的关系类型。这些模型将通过放松这一强有力的假设和开发数据集和模型来推广，这些数据集和模型有助于探索社会关系如何影响社会过程。这项工作将带来具有更强预测能力的新模型，以及关于网络社会动态的新发现和问题。实现这些目标需要从观察数据中推断社会关系的新方法，测试和验证的新资源，以及关系感知社会过程的新模型。 大规模执行所有这些需要机器学习和自然语言处理的新技术，以及人类交互和深度学习的新模型。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

This project aims to advance our understanding of online social structures by systematically incorporating information about the nature of the relationships between people in social systems into network structures in order to study social contagion, thereby helping to establish a foundation for understanding social processes through implicit and explicit relationships. Many social processes, from friendship development to rumor dissemination and peer influence, occur on complex structures of interconnected interactions and relationships of different types among individuals. In order to model such structures in a tractable way, researchers have typically relied on networks or graphs, where nodes represent people and edges encode the idea that two people are socially connected to each other in some way. These simple representations of social connections have allowed us to formulate sophisticated predictive models and algorithms and allowed us to understand the mechanisms behind many of these processes like social contagion and information diffusion. However, the ties that connect individuals are markers of potentially rich, multifaceted relationships between two people often with implicit social dimensions, affordances, and constraints that go well beyond what is directly encoded in the network structure alone. Recognizing all the complexity involved in these ties will improve our ability to accurately model and predict many social dynamics that occur on networks. The research consists of three major phases: (1) developing new methods for detecting and quantifying the strength of association between contagions and relationships; (2) developing new models of information diffusion and social contagion on multiplex networks, where edge relationships and topics are able to interact, with the goal of achieving better prediction accuracy, and (3) relaxing the assumption of a static network in order to develop new methods for predicting changes to relationships types and interactional frequencies in dynamic, evolving social networks. Existing models typically assume social dynamics are independent of relationship type. These models will be generalized by relaxing this strong assumption and developing datasets and models that facilitate exploration of how social relationships affect social processes. This work will lead to new models with stronger predictive powers as well as new findings and questions about social dynamics on networks. Achieving these goals involves new methods for inferring social relationships from observational data, new resources for testing and validation, and new models for relationship-aware social processes. Performing all of these at scale requires new techniques from machine learning and natural language processing, and new models of human interaction and deep learning.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.