Structure and Dynamics of Social Networks and Other Networked Systems

首席研究员Newman0109086和他的同事研究真实世界网络系统的结构和功能，特别是但不只是社交网络。一个经验部分涉及发现和分析网络结构，包括科学家之间的合作网络、公司董事网络、个人偏好网络和学术出版物之间的引用网络。所研究的量包括传递性和度分布等局部可观量，以及中心性和群落结构等非局部可观量。研究人员开发了模型来帮助理解网络结构的影响。特别令人感兴趣的是网络的随机图模型、网络弹性的渗流模型以及发生在社交网络上的流行病模型。研究人员开发了新的算法来提取和可视化网络结构，特别是网络中社区的存在和与网络弹性相关的结构属性，如路径计数和中心性度量。对熟人网络结构的了解，对于理解新闻、谣言、消费趋势等信息如何在社会中传播至关重要。同样，人与人之间的身体接触网络控制着疾病的传播方式。如果没有良好的网络模型，正确理解流行病的本质和进展是不可能的。在这个项目中，研究人员确定了有问题的网络结构，并对该结构对信息和疾病传播的影响等进行了建模。除了加强对这些问题的基本了解外，该项目还指出了改变网络结构或动态的方法，以便改善网络传输(在信息的情况下)或减缓网络传输的速度(在流行病的情况下)。例如，对于疾病传播，它可能能够提出有效的免疫目标或减缓疾病传播的教育运动。所开发的新数据资源和分析技术可用于研究出现网络结构过程的其他问题。

Newman0109086 The principal investigator and his colleagues study thestructure and function of real-world networked systems,particularly but not exclusively social networks. An empiricalcomponent is concerned with the discovery and analysis of thestructure of networks, including networks of collaborationbetween scientists, networks of company directors, networks ofpersonal preferences, and networks of citations between academicpublications. Studied quantities include local observables suchas transitivity and degree distribution, and nonlocal ones suchas centrality and community structure. The investigator developsmodels to aid in the understanding of the effects of networkstructure. Of particular interest are random graph models ofnetworks, percolation models of network resilience, and models ofepidemics taking place on social networks. The investigatordevelops new algorithms for extracting and visualizing networkstructure, particularly the existence of communities in networksand structural properties related to network resilience, such aspath counts and centrality measures. A knowledge of the structure of networks of acquaintance iscrucial to the understanding of how information, such as news,rumors, consumer trends, etc., spreads through society.Similarly, networks of physical contact between people govern theway in which diseases spread. A proper understanding of thenature and progress of epidemics is impossible without goodnetwork models. In this project the investigator determines whatthe structure of the networks in question is, and also models theeffect of that structure on, among other things, the spread ofinformation and disease. As well as enhancing basicunderstanding of these problems, the project points to ways inwhich network structure or dynamics can be changed in order toeither improve network transmission (in the case of information)or slow it down (in the case of epidemics). For diseasetransmission, for instance, it may be able to suggest effectivetargets for immunization or education campaigns to slow diseasespread. The new data resources and analysis techniques developedcan be used to study other problems in which network-structuredprocesses arise.