CRII: III: Generative Models for Robust Real-Time Analysis of Complex Dynamic Networks

计算机、信息、生物和社会科学中的许多复杂系统都可以表示为网络，其中节点表示对象，边表示对象之间的关系。这种复杂的网络结构通常会随着时间的推移而不断变化，通过观察不规则时间的事件，例如人与人之间通过消息进行社交互动的网络，组织之间的交易网络以及人与人之间面对面互动的网络。该项目为不断发展的网络制定了一系列不同复杂性的模型，以便在各种应用环境中对这些网络进行强大的实时分析。这种动态网络模型可以用于许多科学学科和公共卫生应用，包括模拟人与人之间的空气传播病毒。该项目培训新的研究生和本科生，包括来自托莱多大学ACM-W分会的女学生，进行涉及各种数据类型和来源的实际数据科学研究。该项目还导致了一个开源Python软件包DyNetworkX的开发，用于分析动态网络沿着动态网络的教育材料，通过一系列讲座和使用DyNetworkX包的实践教程。该项目旨在为连续时间事件创建一系列概率生成模型，的网络，这些网络足够灵活，可以解释真实的网络数据中出现的复杂结构类型，包括节点流行度、社区结构、互惠性和传递性。该项目还寻求开发高效的增量推理算法和离散时间近似，以便对随着时间的推移而迅速变化的超大型社交网络进行实时分析，例如在线社交网络数据。建议的模型范围允许分析师根据特定应用程序的需求权衡灵活性和可扩展性。两个主要的应用程序是有针对性的：预测传染病在物理接近的网络上的传播，以及实时总结和预测在线社交网络活动。该项目的可扩展资产包括新的概率模型和推理算法，DyNetworkX开源软件包以及动态网络的教育材料。这些奖项旨在使计算机和信息科学领域的研究人员和教育工作者以及其他领域的研究人员，如社会和经济科学、软件工程师和从事动态网络数据工作的业余爱好者受益。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。

Many complex systems in the computer, information, biological, and social sciences can be represented as networks with nodes denoting objects and edges denoting relationships between the objects. Such complex network structures often change continuously over time through the observation of events at irregular times, such as networks of social interactions between people via messages, networks of transactions between organizations, and networks of face-to-face interactions between people. This project formulates a range of models of varying complexity for continuously evolving networks to enable robust real-time analysis of these networks in a variety of application settings. Such dynamic network models could be used in many scientific disciplines and in public health applications, including modeling the spread of airborne viruses between people. The project trains new graduate and undergraduate students, including female students from the University of Toledo's ACM-W chapter, in practical data science research involving a variety of data types and sources. The project also results in the development of an open-source Python software package, DyNetworkX, for analyzing dynamic networks along with educational materials on dynamic networks through a series of lectures and hands-on tutorials using the DyNetworkX package.This project aims to create a range of probabilistic generative models for continuous-time event-based networks that are flexible enough to account for the types of complex structures seen in real network data, including node popularity, community structure, reciprocity, and transitivity. The project also seeks to develop efficient incremental inference algorithms and discrete-time approximations that allow for real-time analysis of extremely large social networks that are rapidly changing over time, such as those seen in online social network data. The proposed range of models allows an analyst to trade off flexibility and scalability depending on the needs of a particular application. Two main applications are targeted: prediction of the spread of infectious disease over networks of physical proximity and real-time summarization and prediction of online social network activity. Deliverable assets of the project include new probabilistic models and inference algorithms, the DyNetworkX open-source software package, and educational materials on dynamic networks. These are intended to benefit researchers and educators in the computer and information sciences as well as researchers in other fields such as the social and economic sciences, software engineers, and hobbyists who work with dynamic network data.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.