Collaborative Research: III: Small: Taming Large-Scale Streaming Graphs in an Open World

协作研究：III：小型：在开放世界中驯服大规模流图

• 批准号: 2236579
• 负责人: Xingquan Zhu
• 金额: $ 30万
• 依托单位: Florida Atlantic University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-07-15 至 2026-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2236579&HistoricalAwards=false
• 关键词: Collaborative; Research; III; Small; Taming

Data plays an essential role in shaping social decisions and scientific conclusions. With the abundance of data available, many data-intensive applications involve data with an underlying structure. Graphs provide a natural mathematical language to precisely describe this structure. Graph data have a ubiquitous presence in our daily lives, found in hydrological systems, transportation networks, cellular networks, social media, and the Web, among many others. Graph Learning (GL) is a crucial research area that focuses on processing graph signals and building predictive models on graph data, and has become a key topic in statistical modeling, data science, data mining, machine learning, and computer science in general. Despite considerable progress, traditional GL algorithms commonly assume that the important factors of the graph data remain unchanged during the learning process. Such static and closed assumptions tend to offer an overly simplified abstraction of complicated tasks in the real world, making GL models fail to characterize and express the data generated from natural or societal phenomena that constantly evolve. The project’s overarching goal is to provide generic solutions to these core issues. Specific applications studied in this project include the development of better approaches for monitoring waterbody impairment and detecting malicious behaviors and cyber-attacks in a timely manner. This project will also provide training opportunities for both graduate and undergraduate researchers in computer science. There will be a specific emphasis on gender diversity and participation of underrepresented groups, allowing individuals from diverse backgrounds to contribute to the advancement of GL research.This collaborative project aims to build a new, holistic, and standardized Graph Learning (GL) framework. The project focuses on open-world and streaming network (OWSN) learning, which considers the evolution of graph data over time in four critical factors: nodal features, topological structures, target labels, and graph domains. To achieve this goal, the project seeks to address fundamental challenges and answer research questions aligned in two threads. The first thread is Graph Representation, which aims to answer fundamental questions such as how to characterize nodes with complex and ever-growing contents using vector representations, and how to delineate the underlying process that drives the evolution of graph topologies. The second thread is Graph Predictive Modeling, which addresses how a graph learner can identify the emergence of new and unknown classes and adapt to them without sacrificing performance on other known classes, and how to generalize to other disparate graph domains in an unsupervised manner. To address these questions, the project integrates tools and advances from diverse areas, such as online optimization, uncertainty quantification, variational analysis, and decision theory. The aim is to deepen the understanding of graph data analysis and shed new light on related questions in these areas. Real-world data from engineering applications, including hydrological system data and computer network data, will be used to extensively evaluate progress in each of the above themes. Collaboration with domain experts in the specified application areas will ensure that the new theory, tools, and software emerging from this project lead to meaningful societal benefits.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.

数据在形成社会决策和科学结论方面发挥着重要作用，许多数据密集型应用程序都涉及具有底层结构的数据，图提供了一种自然的数学语言来精确描述这种结构。在我们的日常生活中，图学习（GL）存在于水文系统、交通网络、蜂窝网络、社交媒体和网络等领域，是一个重要的研究领域，专注于处理图信号和基于图数据构建预测模型。 ，并成为一个关键话题在统计建模、数据科学、数据挖掘、机器学习和计算机科学中，尽管取得了相当大的进步，但传统的 GL 算法通常假设图数据的重要因素在学习过程中保持不变。 GL 模型对现实世界中的复杂任务提供了过于简化的抽象，从而无法表征和表达不断发展的自然或社会现象所生成的数据。该项目的总体目标是为这些核心问题的研究提供通用解决方案。该项目包括开发更好的方法监测水体损害并及时检测恶意行为和网络攻击该项目还将为计算机科学领域的研究生和本科生研究人员提供培训机会，特别强调性别多样性和代表性不足的群体的参与。来自不同背景的人为 GL 研究的进步做出贡献。这个合作项目旨在构建一个新的、整体的、标准化的图学习（GL）框架。该项目专注于开放世界和流网络（OWSN）学习，该框架考虑了图数据随时间的演变四个关键因素：节点特征、拓扑结构、目标标签和图域。为了实现这一目标，该项目致力于解决基本挑战并回答两个线程中的研究问题。第一个主题是图表示，旨在回答。基本问题，例如如何使用向量表示来表征具有复杂且不断增长的内容的节点，以及如何描述驱动图拓扑演化的底层过程。第二个主题是图预测建模，它解决了图学习者如何识别的问题。的出现新的和未知的类并在不牺牲其他已知类的性能的情况下适应它们，以及如何以无监督的方式推广到其他不同的图域。为了解决这些问题，该项目集成了来自不同领域的工具和进步，例如在线优化、不确定性量化、变分分析和决策理论，旨在加深对图数据分析的理解，并为这些领域的相关问题提供新的思路，包括水文系统数据和计算机网络数据。主要用于评估进度在上述每个主题中，与特定应用领域的领域专家的合作将确保该项目中出现的新理论、工具和软件带来有意义的社会效益。该奖项符合 NSF 的法定使命，并被认为值得支持。通过使用基金会的智力优点和更广泛的影响审查标准进行评估。