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

基本信息

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

项目摘要

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.

图形在无数的高影响力领域中无处不在，例如社交媒体平台，协作网络，生物网络和关键的基础架构系统。近年来，在开发深度学习算法（尤其是图形卷积网络-GCNS）中，研究兴趣激增。通过堆叠神经网络原始层的多层，GCN以端到端的方式学习高级功能表示和与图形相关的应用程序，从而在各种学习任务中实现了卓越的性能。特别是，图形卷积和图池操作被认为是GCN的基本构件。但是，绝大多数现有的图形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，它们不足以应对现实图表带来的基本挑战并提高高影响力图挖掘应用程序。该项目的主要目标是开发新颖的操作，以改善图形深度学习算法的重要组成部分，推动最先进的图形挖掘和深度学习研究，并向新的边界发展，并从不同的学科中推进与图形相关的应用程序。本项目提出的一类新颖的图形卷积和构建的构建以及从现实范围内的构建范围以及从现实界定的构建范围，并可以从现实中构建不同的范围，并具有不同的范围，并构建了不同的范围，并具有不同的范围，并可以从现实界定范围，并从现实中构建了范围，并构建了更多的范围。处理来自不同域的高影响力图应用。首先，它开发了一个可训练的图形卷积操作系列，该家族可以从特征级，边缘级别和节点级别的不同方面整合现实世界图的属性。其次，它研究了图形池的问题，以支持图形级别的分析任务，并根据节点采样和节点群集开发新颖的拓扑感知图池池操作。第三，它通过为各种常见的图形应用程序（例如图异常检测和图形对齐方式）构建更强大和定制的深度学习体系结构来评估所提出的图形卷积和图形合并操作的影响。该项目将与新开发的本科生和研究生课程紧密融合。该项目的结果和发现将通过公共数据集，开源软件存储库，期刊和会议出版物，特殊用途的讲习班或教程以及教育和外展活动进行传播。该奖项反映了NSF的法定任务，并通过该基金会的知识优点和广泛的效果来评估，这是通过评估值得通过评估来支持的。