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.

数据在塑造社会决策和科学结论中起着至关重要的作用。随着可用数据的抽象，许多数据密集型应用程序涉及具有基础结构的数据。图提供了一种自然的数学语言，可以精确描述这种结构。图形数据在我们的日常生活中存在着无处不在的存在，在水文系统，传输网络，蜂窝网络，社交媒体和网络中都存在。图形学习（GL）是一个至关重要的研究领域，侧重于处理图形信号并在图形数据上构建预测模型，并已成为统计建模，数据科学，数据挖掘，机器学习和计算机科学的关键主题。尽管取得了很大的进步，但传统的GL算法通常认为图形数据的重要因素在学习过程中保持不变。这种静态和封闭的假设倾向于在现实世界中提供过度简化复杂任务的抽象，使GL模型无法表征和表达从不断发展的自然或社会现象产生的数据。该项目的总体目标是为这些核心问题提供通用解决方案。该项目研究的特定应用包括开发更好的方法来监测水体障碍，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研究人员提供培训机会。将要特别强调性别多样性和代表性不足的群体的参与，这使来自多样性背景的个人有助于GL研究的发展。该协作项目旨在建立一个新的，整体和标准化的图形学习（GL）框架。该项目的重点是开放世界和流网络（OWSN）学习，该学习将图形数据随时间推移以四个关键因素的形式进行：节点特征，拓扑结构，目标标签和图形域。为了实现这一目标，该项目旨在应对基本挑战，并回答以两个线程对齐的研究问题。第一个线程是图表表示，旨在回答基本问题，例如如何使用矢量表示使用复杂且不断增长的内容来表征节点，以及如何描述驱动图形拓扑演化的基础过程。第二个线程是图表预测建模，它解决了图形学习者如何识别新的和未知类的出现并适应它们而不牺牲其他已知类别的性能，以及如何以无聊的方式推广到其他不同的图形域。为了解决这些问题，该项目整合了潜水区域的工具和进步，例如在线优化，不确定性量化，变异分析和决策理论。目的是加深对图形数据分析的理解，并在这些领域的相关问题中发明新的启示。来自工程应用程序的现实世界数据，包括液压系统数据和计算机网络数据，将用于广泛评估上述每个主题的进度。与指定应用领域的领域专家合作将确保该项目的新理论，工具和软件带来有意义的社会福利。该奖项反映了NSF的法定任务，并被认为是通过基金会的知识分子优点和更广泛影响的评估来评估的审查标准。