权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

III: Medium: Collaborative Research: Towards Scalable and Interpretable Graph Neural Networks

III：媒介：协作研究：迈向可扩展和可解释的图神经网络

基本信息

批准号：
1955189
负责人：
Shuiwang Ji
金额：
$ 10万
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：
2020-07-15 至 2022-06-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1955189&HistoricalAwards=false
关键词：
III Medium Collaborative Research Towards

项目摘要

Graphs are ubiquitous data structures in numerous domains, such as social science (social networks), natural science (physical systems, and protein-protein interaction networks) and knowledge graphs. As new generalizations of traditional deep neural networks to graph structured data, Graph Neural Networks (or GNNs) have demonstrated the power in graph representation learning and have permeated numerous areas of science and technology. However, GNNs also inherited the drawback of traditional deep neural networks, i.e., lacking interpretability. Moreover, the complexity of graph data introduces the scalability as a new limitation for GNNs because graph structured data are not independent. These drawbacks have raised tremendous concerns to adopt GNNs in many critical applications pertaining to fairness, privacy, and safety. Thus, this project aims to tackle the major drawbacks of GNNs and greatly enlarge their usability in critical applications. To achieve the research goal, this project systematically investigates advanced principles for scalable GNNs and new mechanisms to interpret GNNs. The proposed research extends the state-of-the-art GNNs to a new frontier, investigates original problems that entreat innovative solutions and paves the way for a new research endeavor effectively tame graph mining. As many real-world domains problems requires scalable and interpretable graph mining techniques, the project has potential to benefit many real-world applications from various disciplines such as Computer Science, Social Science, Healthcare and Bioinformatics. This project proposes novel principles and mechanisms for scalable and interpretable graph neural networks to facilitate the adoption of GNNs on critical domains, investigates associated fundamental research issues and develops effective algorithms. The project offers the first comprehensive investigation on these directions, and the designed novel methodologies and tasks will deepen our understanding on the inner working mechanisms of GNNs and contribute to real-world applications. The success of this project will be (1) New scalable and interpretable GNNs with state-of-the-art graph representation learning and predictive performance; (2) Theoretical analysis such as convergence and complexity; and (3) Open-source implementations of all key algorithms and frameworks. Disparate means are planned to disseminate the project and its findings, such as web enabled data and software repositories, books, journal and conference publications, special purpose workshops or tutorials, and industrial collaborations. The project can be effectively integrated to undergraduate and graduate courses as well as in student research projects.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.

图是许多领域中普遍存在的数据结构，例如社会科学（社交网络）、自然科学（物理系统和蛋白质-蛋白质相互作用网络）和知识图。作为传统深度神经网络对图结构数据的新概括，图神经网络（或GNN）已经展示了图表示学习的强大功能，并已渗透到许多科学和技术领域。然而，GNN也继承了传统深度神经网络的缺点，即，缺乏可解释性。此外，图数据的复杂性引入了可扩展性作为GNN的新限制，因为图结构化数据不是独立的。这些缺点引起了人们对在许多与公平、隐私和安全有关的关键应用中采用GNN的极大关注。因此，该项目旨在解决GNN的主要缺点，并大大提高其在关键应用程序中的可用性。为了实现研究目标，本项目系统地研究了可扩展GNN的高级原理和解释GNN的新机制。拟议的研究将最先进的GNN扩展到一个新的前沿，研究了需要创新解决方案的原始问题，并为有效驯服图挖掘的新研究奋进铺平了道路。由于许多现实领域的问题需要可扩展和可解释的图挖掘技术，该项目有可能使计算机科学，社会科学，医疗保健和生物信息学等各个学科的许多现实应用程序受益。该项目为可扩展和可解释的图神经网络提出了新的原理和机制，以促进GNN在关键领域的采用，研究相关的基础研究问题并开发有效的算法。该项目首次对这些方向进行了全面的研究，设计的新颖方法和任务将加深我们对GNN内部工作机制的理解，并有助于实际应用。该项目的成功将是（1）新的可扩展和可解释的GNN，具有最先进的图表示学习和预测性能;（2）理论分析，如收敛性和复杂性;以及（3）所有关键算法和框架的开源实现。计划采用不同的方式传播该项目及其成果，如网络数据和软件储存库、书籍、期刊和会议出版物、特殊目的讲习班或教程以及行业合作。该项目可以有效地整合到本科生和研究生课程以及学生的研究项目中。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。