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

CRII：III：复杂动态网络鲁棒实时分析的生成模型

• 批准号: 1755824
• 负责人: Kevin Xu
• 金额: $ 17.46万
• 依托单位: University of Toledo
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-07-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1755824&HistoricalAwards=false
• 关键词: CRII; III; Generative; Models; Robust

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.

计算机，信息，生物学和社会科学中的许多复杂系统都可以表示为带有节点的网络，表示对象和边缘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流逝而在不规则时期观察到事件，例如通过消息之间的社交互动网络，组织之间的交易网络以及人与人之间面对面互动的网络。该项目为连续发展的网络制定了一系列不同复杂性的模型，以在各种应用程序设置中对这些网络进行强大的实时分析。这种动态网络模型可用于许多科学学科和公共卫生应用中，包括对空中病毒之间的传播建模。该项目在涉及各种数据类型和来源的实用数据科学研究中训练新的研究生和本科生，包括来自托莱多ACM-W分会的女学生。 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结构，互惠和传递性。该项目还旨在开发有效的增量推理算法和离散的时间近似，这些算法允许对随着时间的推移迅速变化的极大社交网络进行实时分析，例如在线社交网络数据中所看到的。提出的模型范围允许分析师根据特定应用的需求进行权衡灵活性和可伸缩性。两个主要应用是针对性的：预测传染病在身体接近和实时摘要网络上的传播以及在线社交网络活动的预测。该项目的可交付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，例如社会和经济科学，软件工程师和与动态网络数据一起工作的业余爱好者。该奖项反映了NSF的法定任务，并被认为是通过基金会的智力功能和广泛影响的评估来审查CRITERIA的评估。

项目成果

A Mutually Exciting Latent Space Hawkes Process Model for Continuous-time Networks

• DOI: 10.48550/arxiv.2205.09263
• 发表时间: 2022-05
• 期刊: ArXiv
• 作者: Zhipeng Huang;Hadeel Soliman;Subhadeep Paul;Kevin S. Xu

Analyzing escalations in militarized interstate disputes using motifs in temporal networks

使用时间网络中的主题分析军事化国家间争端的升级

• DOI: 10.1007/978-3-030-93409-5_44
• 发表时间: 2022
• 期刊: Proceedings of the 10th International Conference on Complex Networks and Their Applications
• 作者: Do, Hung N.;Xu, Kevin S.
• 通讯作者: Xu, Kevin S.

Counteracting filter bubbles with homophily-aware link recommendations

通过同质感知链接推荐来消除过滤气泡

• DOI: 10.1007/978-3-031-17114-7_15
• 发表时间: 2022
• 期刊: Lecture notes in computer science
• 作者: Warton, Robert;Volny, Chris;Xu, Kevin S.
• 通讯作者: Xu, Kevin S.

A hybrid adjacency and time-based data structure for analysis of temporal networks

用于分析时态网络的混合邻接和基于时间的数据结构

• DOI: 10.1007/978-3-030-93409-5_49
• 发表时间: 2022
• 期刊: Proceedings of the 10th International Conference on Complex Networks and Their Applications
• 作者: Hilsabeck, Tanner;Arastuie, Makan;Xu, Kevin S.
• 通讯作者: Xu, Kevin S.

Personalized Degrees: Effects on Link Formation in Dynamic Networks from an Egocentric Perspective

• DOI: 10.1145/3308560.3316699
• 发表时间: 2017-12
• 期刊: Companion Proceedings of The 2019 World Wide Web Conference
• 作者: Makan Arastuie;Kevin S. Xu