CRII:III:Towards Advanced Filtering and Pooling Operations for Graph Neural Networks

CRII:III：走向图神经网络的高级过滤和池化操作

• 批准号: 2406647
• 负责人: Yao Ma
• 金额: $ 17.5万
• 依托单位: Rensselaer Polytechnic Institute
• 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: 2023-10-15 至 2025-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2406647&HistoricalAwards=false
• 关键词: CRII; III; Towards; Advanced; Filtering

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).In recent years, we have witnessed a rapid growth in our ability to generate and gather data from numerous platforms in the online world and various sensors in the physical world. Graphs provide a universal representation for a variety of data including online social networks, knowledge graphs, transportation networks, and chemical compounds. Entities can usually be represented as nodes while their relations can be denoted represented as edges. Many important real-world applications on these data can be treated as computational tasks on graphs. A crucial step to facilitate these tasks is to learn good vector representations either for nodes or graphs. Recently, graph neural networks, which generalize deep learning techniques to graphs, have been widely adopted to learning representations for graphs. Though graph neural networks have advanced numerous real-world applications from various fields, they still suffer from many limitations in terms of efficacy and efficiency. This project aims to address these limitations by conducting theoretical analysis and developing innovative algorithms. This project is specifically motivated by applications to computational social science, computational biology, and fraud detection in e-commerce. Furthermore, this project will involve graduate and undergraduate students in pursuing their theses or honor projects. Discoveries and research findings of this project will be tightly integrated into several current and new courses at the New Jersey Institute of Technology.The technical aims of the project are divided into two tasks corresponding to the two major building components of graph neural networks: graph filtering operations and graph pooling operations. The graph filtering operation aims to refine node representations for all nodes in a graph. On the other hand, the graph pooling operation aims to summarize node representations to obtain a graph representation. The first task aims to investigate graph filtering operations under heterophily—a setting typically poses great challenges for graph filtering operations. In particular, the investigator will conduct theoretical analyses on graph filtering operations to gain deeper insights into their intrinsic mechanism, especially under the scenario of heterophily. Then, based on these understandings, more advanced graph neural networks models will be proposed to handle heterophilous graphs. The second task aims to develop more efficient and effective graph pooling operations. The investigators will explore and develop graph pooling operations based on clustering and down-sampling process. To improve the efficacy and efficiency of the graph pooling operations, the clustering/down-sampling process will be nicely incorporated into the entire learning framework in an end-to-end way.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.

该奖项的全部或部分资金来自《2021年美国救援计划法案》(公法117-2)。近年来，我们见证了我们从网络世界的众多平台和物理世界的各种传感器生成和收集数据的能力的快速增长。图为各种数据提供了通用的表示，包括在线社交网络、知识图、交通网络和化合物。实体通常可以表示为节点，而它们的关系可以表示为边。在这些数据上的许多重要的现实应用程序可以被视为图上的计算任务。简化这些任务的关键一步是学习节点或图的良好的矢量表示。近年来，将深度学习技术推广到图中的图神经网络已被广泛用于学习图的表示法。尽管图神经网络已经在各个领域推进了大量的实际应用，但它们在有效性和效率方面仍然受到许多限制。该项目旨在通过进行理论分析和开发创新算法来解决这些限制。这个项目的具体动机是应用于计算社会科学、计算生物学和电子商务中的欺诈检测。此外，这个项目将包括研究生和本科生进行他们的论文或荣誉项目。该项目的发现和研究成果将被紧密地整合到新泽西理工学院的几门现有课程和新课程中。该项目的技术目标被分为两个任务，分别对应于图神经网络的两个主要构建组件：图过滤操作和图池操作。图形过滤操作旨在优化图形中所有节点的节点表示。另一方面，图池操作旨在汇总节点表示以获得图表示。第一个任务旨在研究异形环境下的图过滤操作--这一环境通常会给图过滤操作带来巨大的挑战。特别是，研究者将对图过滤操作进行理论分析，以更深入地了解其内在机制，特别是在异形性的情况下。然后，在这些认识的基础上，提出更先进的图神经网络模型来处理异构图。第二项任务旨在开发更高效和有效的图池操作。研究人员将探索和开发基于聚类和下采样过程的图形池化操作。为了提高图表汇集操作的效果和效率，聚类/下采样过程将以端到端的方式很好地整合到整个学习框架中。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。