CAREER: Modern Machine Learning on Graphs: From Theory to Practice

图形或网络为整个科学领域的数据建模提供了一个基本工具。例如，图形可以用来表示人及其社会关系，蛋白质及其相互作用，以及大脑神经元和连接体。许多现实世界的应用涉及基于图结构数据解决预测任务，如网络异常检测、推荐、药物设计、材料特性分析和粒子碰撞去噪。因此，至关重要的是开发强大的和值得信赖的算法工具，可以从图结构数据中学习。该项目旨在解决现代图形结构数据的新兴复杂特征所带来的挑战。首先，该项目将专注于从传统图模型经常错过的高阶关系中学习。其次，它将探索图结构数据的深度学习模型。这两个方向都追求既有理论原则又有实践意义的方法。该项目的成功将导致对社会和生物网络的理解以及高能物理数据处理质量的提高。此外，该项目将促进数据科学，计算行业和领域科学家之间的合作。该项目的技术贡献分为两个相互关联的主题：（i）开发具有快速学习算法的表达超图模型。本研究主题提出研究超图扩散问题，其中每个超边与一个非线性扩散函数相关联，该函数可以手工制作或学习来模拟复杂的高阶关系。(ii)开发可证明表达和可推广的图神经网络（GNN）模型。本研究提出了一种基于结构特征增强的GNN结构，分析并提高其泛化能力。开发的方法将在图学习任务中进行评估，如节点分类，链接预测，图异常检测和高能物理中出现的问题。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估来支持。

Graphs or networks provide a fundamental tool to model data throughout the sciences. For instance, graphs can be used to represent people and their social relations, proteins and their interactions, and brain neurons and the connectome. Many real-world applications involve solving prediction tasks based on graph-structured data such as network anomaly detection, recommendation, drug design, material property analysis, and particle collision denoising. Therefore, it is crucial to develop robust and trustworthy algorithm tools that may learn from graph-structured data. This project aims to address the challenges posed by the emerging complex features of modern graph-structured data. Firstly, the project will focus on learning from higher order relations that are often missed by traditional graph models. Secondly, it will explore deep learning models for graph-structured data. Both directions pursue the approaches that are both principled in theory and useful in practice. The success of this project will lead to an improvement of the understanding of social and biological networks and the quality of data processing for high-energy physics. Moreover, this project will foster collaborations across college students in data science, the computing industry, and domain scientists.The technical contributions of this project are organized into two interrelated themes: (i) Developing expressive hypergraph models with their fast-learning algorithms. This research theme proposes to study hypergraph diffusion problems, where each hyperedge is associated with a nonlinear diffusion function that can be either handcrafted or learned to model complex higher-order relations. (ii) Developing provably expressive and generalizable graph neural network (GNN) models. This research theme proposes to design provably more expressive GNN architectures based on a framework of structural feature augmentation, analyze and improve their generalization capability. The developed approaches will be evaluated in the graph learning tasks such as node classification, link prediction, graph anomaly detection, and emerging problems in high-energy physics.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.