Collaborative: DHB: Social Network Dynamics of Youth

人们对社交网络有着广泛的兴趣：在线服务促进了管理它们的工具，流行病学家用它们来模拟疾病传播，企业跟踪它们以提高效率，学校研究人员用它们来解释性能（以及许多其他应用）。 虽然对静态网络性质的研究已经很成熟，但研究人员对社交网络如何形成、持续以及随着时间的推移如何影响人们知之甚少。 研究社会网络动态的部分困难在于缺乏合适的数据，因为很难在无限的人群中跟踪网络进化。 学校为研究网络动态提供了一个很好的环境。 首先，学校提供了关注网络动态的自然边界。 第二，关系内容随着年龄的增长而发展，从儿童之间的局部同学关系转变为年轻人之间更深层次的友谊和浪漫关系。 最后，年轻人直接关注网络变化，他们经常对他人网络关系的变化做出反应。 在这里，研究人员利用数百所学校和数千名学生的数据，将多项关于青年网络的研究编织在一起，构建了一幅复合画像，展示了长期社交网络是如何从短暂的面对面互动中发展起来的。 为了构建这幅画像，研究人员使用了新的动态网络工具，使他们能够直接可视化网络演化。 就像心电图可以让医生监测心脏对压力的反应一样，这些“网络电影”可以帮助研究人员识别网络形成的社会机制。 接下来，研究人员开发了新的聚类技术，以识别群体及其随时间的稳定性。这些聚类技术揭示了对等群体的生活史，并提供了一个关键的网络变化的中期图像。最后，他们使用一类新的模型，称为指数随机图模型（ERGM），以确定影响网络形成和变化的社会机制的类别。社交网络的高度相互依赖性使其不适合标准统计建模，但指数随机图模型解决了网络数据中存在的各种相互依赖性。 虽然标准特征--比如倾向于与与自己相似的人建立关系（“同质性”）--被认为是重要的，但内部网络特征也将是重要的。 这些特征解释了年轻人如何改变他们的本地网络，以应对网络中其他地方的变化。 例如，当一个群体的成员与另一个群体的成员成为朋友时，其他关系会发生什么变化？ 能够解释关系是如何形成的，是什么使同龄群体稳定，以及受欢迎程度如何变化，对教育工作者和政策制定者与青年网络合作的能力有直接影响，以帮助促进亲社会，健康的青年行为。

There is broad general interest in social networks: online services promote tools to manage them, epidemiologists use them to model disease transmission, businesses track them for efficiency, and school researchers use them to explain performance (among many other applications). Although research on the properties of static networks is well-developed, researchers know very little about how social networks form, persist, and influence people over time. Part of the difficulty in studying social network dynamics is a lack of suitable data, as it is difficult to follow network evolution in unbounded populations. Schools provide an excellent context for studying network dynamics. First, schools provide natural boundaries that focus network dynamics. Second, relation content evolves developmentally as youths age, changing from local classmate relations among children to deeper friendship and romantic relations among young adults. Finally, youths are directly concerned with network change, and they often respond to changes in others' network ties. Here, the investigators weave together multiple studies of youth networks using data on hundreds of schools and thousands of students to build a composite portrait illustrating how long-term social networks develop from transitory face-to-face interactions. To build this portrait, the researchers use new dynamic network tools allowing them to visualize network evolution directly. Just as a cardiogram allows physicians to monitor the dynamics of heart functioning in response to stress, these "network movies" help researchers identify the social mechanisms responsible for network formation. The investigators next develop new clustering techniques that identify groups and their stability over time. These clustering techniques reveal peer group life-histories, and it provides a key middle-range image of network change. Finally, they use a new class of models known as exponential random graph models (ERGM) to identify classes of social mechanisms affecting network formation and change. The highly interdependent nature of social networks has made them inappropriate for standard statistical modeling, but exponential random graph models address the kinds of interdependencies present in network data. While standard features -- such as a preference to form relations with people similar to oneself ("homophily") -- are expected to be important, internal network features will be important as well. These features account for how youths change their local networks in response to changes elsewhere in the network. For example, how do other relations change when a member of one group becomes friends with members in another group? Being able to explain how relationships form, what makes peer groups stable, and how popularity changes has direct influence on educators' and policy makers' ability to work with youth networks to help promote pro-social, healthy youth behavior.