"Structure and Dynamics of Social Networks and Other Networked Systems."

首席研究员和他的同事研究现实世界网络系统的结构和功能，特别是信息、计算机和社会网络。这项工作包括一个与网络结构的发现和分析有关的经验部分，包括合作网络、论文和注册专利的引用网络、运输和其他地理网络、社交网络和各种类型的计算机网络，包括管理员网络和服务器-客户端网络以及“网络博客”网络。所研究的性质包括关联函数、空间结构、中心度和相似性度量、分类综合和群落结构度量。这项工作还有一个理论部分，涉及模型的开发，以帮助理解网络结构的影响。特别感兴趣的是网络的广义随机图模型、网络弹性的渗流模型、增长模型、指数随机图和马尔可夫图。该项目还包括开发新的计算机算法，用于提取和可视化网络结构，特别是用于检测网络中的社区结构。研究人员和同事试图了解网络的形式和行为，特别是信息网络、计算机网络和社交网络。这项工作涉及到许多当前重要的问题。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，计算机网络控制着计算机病毒的传播。如果没有好的网络模型，就不可能正确理解感染的性质和进展。这里描述的工作既是为了确定所讨论的网络结构是什么，也是为了模拟该结构对可能的结果的影响。除了加强对这些问题的基本了解外，这些结果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，可以建议有效的免疫目标或教育活动来减缓疾病传播。还开发了新的数据资源和分析技术，这将有助于其他研究人员未来在相关主题上的工作。

The principal investigator and his colleagues study thestructure and function of real-world networked systems,particularly information, computer, and social networks. Thework includes an empirical component concerned with the discoveryand analysis of network structure, including collaborationnetworks, citation networks of papers and of registered patents,transportation and other geographic networks, social networks,and computer networks of various kinds including administratorand server-client networks and networks of "weblogs." Propertiesstudied include correlation functions, spatial structure,centrality and similarity measures, and measures of assortativemixing and community structure. There is also a theoreticalcomponent to the work, involving the development of models to aidin the understanding of the effects of network structure. Ofparticular interest are generalized random graph models ofnetworks, percolation models of network resilience, growthmodels, exponential random graphs, and Markov graphs. Theproject also includes the development of new computer algorithmsfor extracting and visualizing network structure, particularlyfor the detection of community structure in networks. The investigator and colleagues try to understand the formand behavior of networks, particularly information networks,computer networks, and social networks. The work has bearing on anumber of issues of current importance. A knowledge of thestructure of networks of acquaintance is crucial to anunderstanding of how information, such as news, rumors, consumertrends, etc., spreads through society. Similarly, networks ofphysical contact between people govern the way in which diseasesspread and computer networks govern the spread of computerviruses. A proper understanding of the nature and progress ofinfections is impossible without good network models. The workdescribed here is aimed both at determining what the structure ofthe networks in question is, and also at modeling the effect ofthat structure on possible outcomes. As well as enhancing basicunderstanding of these problems, the results point 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 possible to suggesteffective targets for immunization or education campaigns to slowdisease spread. New data resources and analysis techniques alsoare developed that will aid future work by other researchers onrelated topics.