III: Small: Collaborative Research: Demystifying Deep Learning on Graphs: From Basic Operations to Applications

III：小：协作研究：揭秘图深度学习：从基本操作到应用

• 批准号: 2006861
• 负责人: Shuiwang Ji
• 金额: $ 23.13万
• 依托单位: Texas A&M Engineering Experiment Station
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2006861&HistoricalAwards=false
• 关键词: III; Small; Collaborative; Research; Demystifying

Graphs are ubiquitous in myriad high-impact domains, e.g., social media platforms, collaboration networks, biological networks, and critical infrastructure systems. Recent years have witnessed a surge of research interests in developing deep learning algorithms (in particular graph convolution networks - GCNs) for graph data. By stacking multiple layers of neural network primitives, GCNs learn high-level feature representations and address graph-related applications in an end-to-end manner, achieving superior performance in various learning tasks. In particular, the graph convolution and graph pooling operations are considered as fundamental building blocks of GCNs. However, a vast majority of existing graph convolution and graph pooling operations are simple extensions of the corresponding operations from convolution neural networks. Therefore, they are insufficient to tackle the fundamental challenges brought by real-world graphs and advance high-impact graph mining applications. The primary goal of this project is to develop novel operations to improve the essential building blocks of deep learning algorithms for graphs, propelling the state-of-the-art graph mining and deep learning research to a new frontier and advancing graph-related applications from different disciplines.This project proposes a class of novel graph convolution and pooling operations that can faithfully characterize the properties of real-world graphs from different perspectives, and build more tailored and powerful deep architectures in handling high-impact graph applications from different domains. First, it develops a family of trainable graph convolution operations that can integrate properties of real-world graphs from different aspects at the feature-level, edge-level, and node-level. Second, it investigates the problem of graph pooling to support graph-level analytical tasks and develops novel topology-aware graph pooling operations based on node sampling and node clustering. Third, it assesses the impact of proposed graph convolution and graph pooling operations by building more powerful and customized deep learning architectures for various common graph applications, such as graph anomaly detection and graph alignment. This project will be tightly integrated with newly developed undergraduate and graduate courses. The results and findings of this project will be disseminated through public datasets, open-source software repositories, journal and conference publications, special-purpose workshops or tutorials, as well as education and outreach activities.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.

图无处不在的无数高影响力的领域，例如，社交媒体平台，协作网络，生物网络，和关键的基础设施系统。近年来，人们对开发用于图数据的深度学习算法（特别是图卷积网络- GCNs）的研究兴趣激增。通过堆叠多层神经网络原语，GCNs以端到端方式学习高级特征表示和解决与图相关的应用，在各种学习任务中获得卓越的性能。其中，图卷积和图池运算被认为是GCNs的基本组成部分。然而，绝大多数现有的图卷积和图池操作都是卷积神经网络相应操作的简单扩展。因此，它们不足以解决现实世界图所带来的根本挑战，也不足以推进高影响力的图挖掘应用。该项目的主要目标是开发新的操作，以改进图的深度学习算法的基本构建块，将最先进的图挖掘和深度学习研究推向一个新的前沿，并推进不同学科的图相关应用。该项目提出了一类新颖的图卷积和池化操作，可以从不同的角度忠实地表征现实世界图的属性，并在处理来自不同领域的高影响力图应用程序时构建更定制和强大的深度架构。首先，它开发了一系列可训练的图卷积操作，可以在特征级，边缘级和节点级从不同方面整合现实世界图的属性。其次，研究了支持图级分析任务的图池化问题，并基于节点采样和节点聚类开发了新颖的拓扑感知图池化操作。第三，通过为各种常见的图应用（如图异常检测和图对齐）构建更强大和定制的深度学习架构，评估所提出的图卷积和图池操作的影响。本项目将与新开设的本科和研究生课程紧密结合。该项目的结果和发现将通过公共数据集、开放源码软件库、期刊和会议出版物、特殊目的讲习班或教程以及教育和外联活动进行传播。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Topology-Aware Graph Pooling Networks

• DOI: 10.1109/tpami.2021.3062794
• 发表时间: 2021-12-01
• 期刊: IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
• 影响因子: 23.6
• 作者: Gao, Hongyang;Liu, Yi;Ji, Shuiwang
• 通讯作者: Ji, Shuiwang

Neighbor2Seq: Deep Learning on Massive Graphs by Transforming Neighbors to Sequences

• DOI: 10.1137/1.9781611977172.7
• 发表时间: 2022-02
• 作者: Meng Liu;Shuiwang Ji

Efficient and Equivariant Graph Networks for Predicting Quantum Hamiltonian

• DOI: 10.48550/arxiv.2306.04922
• 发表时间: 2023-06
• 作者: Haiyang Yu;Zhao Xu;X. Qian;Xiaoning Qian;Shuiwang Ji

Learning Fair Graph Representations via Automated Data Augmentations

• 发表时间: 2023
• 作者: Hongyi Ling;Zhimeng Jiang;Youzhi Luo;S. Ji;Na Zou

Group Equivariant Fourier Neural Operators for Partial Differential Equations

• DOI: 10.48550/arxiv.2306.05697
• 发表时间: 2023-06
• 作者: Jacob Helwig;Xuan Zhang;Cong Fu;Jerry Kurtin;Stephan Wojtowytsch;Shuiwang Ji