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

图在无数高影响力领域中无处不在，例如社交媒体平台、协作网络、生物网络和关键基础设施系统。近年来，人们对图形数据的深度学习算法(尤其是图形卷积网络)的研究兴趣激增。通过堆叠多层神经网络基元，GCNS以端到端的方式学习高级特征表示并处理与图相关的应用，从而在各种学习任务中获得优异的性能。特别地，图卷积和图池操作被认为是GCNS的基本构建块。然而，现有的绝大多数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带来的根本挑战，也不足以推进高影响的图挖掘应用。该项目的主要目标是开发新的操作来改进图的深度学习算法的基本构件，将最新的图挖掘和深度学习研究推向新的前沿，并从不同的学科推进与图相关的应用。本项目提出了一类新的图卷积和池化操作，能够从不同的角度真实地刻画真实世界的图的性质，并构建更定制和更强大的深层体系结构来处理来自不同领域的高影响图应用。首先，它开发了一类可训练的图卷积运算，可以在特征级、边级和节点级从不同方面综合真实世界图的性质。其次，研究了支持图级分析任务的图池问题，提出了基于节点采样和节点聚类的拓扑感知图池操作。第三，通过为图异常检测和图对齐等各种常见的图应用程序构建更强大和定制的深度学习体系结构，评估了所提出的图卷积和图池操作的影响。该项目将与新开发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、开源软件库、期刊和会议出版物、特殊目的研讨会或教程以及教育和外联活动进行传播。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。

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.