CAREER: Spatial Network Deep Generative Modeling, Transformation, and Interpretation

职业：空间网络深度生成建模、转换和解释

• 批准号: 2113350
• 负责人: Liang Zhao
• 金额: $ 54.97万
• 依托单位: Emory University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-10-01 至 2025-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2113350&HistoricalAwards=false
• 关键词: CAREER; Spatial; Network; Deep; Generative

As we enter the modern big data era, spatial data and network data are popular types of high-dimensional data with continuous and discrete properties, respectively. Spanning these two data types, spatial networks represent a crucial data structure where the nodes and edges are embedded in a geometric space. Nowadays, spatial network data is becoming increasingly popular and important, ranging from micro-scale (e.g., protein structures), to middle-scale (e.g., biological neural networks), to macro-scale (e.g., mobility networks). The modeling of spatial networks is extremely difficult due to the significant challenges involved, including: 1) incompatibility between the treatments for continuous spatial properties and discrete network properties, 2) the close interactions between spatial and network topologies, and 3) their extremely high dimensionality. These challenges echo numerous unsolved critical issues in the real world such as modeling and understanding the "protein structure folding process" and "mental disease mechanisms in brain networks". Until now, there has been a significant gap between our lack of powerful models and the extremely complex research issues involved in modeling the generation of spatial networks. To fill this gap, this project focuses on developing a transformative framework for spatial network generative modeling, which can automatically learn the underlying complex generation process from massive spatial network datasets.This project generalizes existing generative models of spatial networks into deep and expressive architectures. The developed framework aims at: 1) automatically learning new generation and transformation process of spatial networks, 2) embedding user-specified principles to constrain and regularize the generated spatial networks, and 3) pursuing the model interpretability and automatically distill new understandable principles of spatial network process. The research activities are conducted along the following themes: i) novel spatial and spectral graph decoders for large spatial networks, ii) deep generative modeling and optimization with spatial and topological constraints and regularization, iii) a variety of novel spatial- and spectral- graph transformation strategies, and iv) a novel system for interacting the predefined and distilled principles between human and models. The techniques developed in this project aim at benefiting various social and natural science domains by enabling efficient and accurate discovery and synthesis of complex spatial network behavior. The success of this project can benefit crucial domains including medicine design, mental disease early diagnoses, and disaster management. Core products of this project, including publications, software, and datasets, are published in various websites with active user support, in order to largely benefit the research communities and the society. The proposed unified framework is also used for teaching spatial and network data mining concepts, as well as providing graduate and undergraduate students with new courses, research, and internship opportunities. This project actively includes underrepresented students and outreach to local high schools.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.

当我们进入现代大数据时代时，空间数据和网络数据分别是具有连续和离散属性的高维数据的流行类型。空间网络跨越这两种数据类型，代表了至关重要的数据结构，其中节点和边缘嵌入了几何空间中。如今，空间网络数据变得越来越流行和重要，从微观（例如蛋白质结构）到中尺度（例如生物神经网络）到宏观尺度（例如，移动性网络）。由于涉及的重大挑战，包括：1）连续空间特性和离散网络属性之间的不兼容，因此空间网络的建模极为困难，2）空间和网络拓扑之间的紧密相互作用，以及3）它们的尺寸极高。这些挑战呼应了现实世界中许多未解决的关键问题，例如建模和理解“蛋白质结构折叠过程”和“脑网络中的精神疾病机制”。到目前为止，我们缺乏强大的模型与建模空间网络的产生涉及的非常复杂的研究问题之间存在很大的差距。为了填补这一空白，该项目着重于为空间网络生成建模开发一个变革性的框架，该框架可以自动从大量的空间网络数据集中学习基础的复杂生成过程。该项目将空间网络的现有生成模型推广到深度和表现力的架构中。开发的框架的目的是：1）自动学习空间网络的新一代和转换过程，2）嵌入用户指定的原理以限制和正规化生成的空间网络，3）追求模型解释性，并自动将空间网络过程的新可理解的可理解的可理解的原理提炼。研究活动沿以下主题进行：i）大型空间网络的新型空间和光谱图解码器，ii）通过空间和拓扑结构和正则化的深层生成建模和优化；该项目中开发的技术旨在通过实现复杂的空间网络行为的有效，准确的发现和综合来使各种社会和自然科学领域受益。该项目的成功可以使包括医学设计，精神疾病早期诊断和灾难管理在内的关键领域受益。该项目的核心产品，包括出版物，软件和数据集，在具有主动用户支持的各个网站上发布，以在很大程度上使研究社区和社会受益。拟议的统一框架还用于教授空间和网络数据挖掘概念，并为研究生和本科生提供新课程，研究和实习机会。该项目积极包括代表性不足的学生和与当地高中的宣传。该奖项反映了NSF的法定任务，并且使用基金会的知识分子优点和更广泛的影响审查标准，被认为值得通过评估来获得支持。

项目成果

Accelerated Gradient-free Neural Network Training by Multi-convex Alternating Optimization

• DOI: 10.1016/j.neucom.2022.02.039
• 发表时间: 2018-11
• 期刊: Neurocomputing
• 影响因子: 6
• 作者: Junxiang Wang;Fuxun Yu;Xiangyi Chen;Liang Zhao

Small molecule generation via disentangled representation learning

• DOI: 10.1093/bioinformatics/btac296
• 发表时间: 2022-05
• 期刊: Bioinformatics
• 影响因子: 5.8
• 作者: Yuanqi Du;Xiaojie Guo;Yinkai Wang;Amarda Shehu;Liang Zhao

Deep Multi-attributed Graph Translation with Node-Edge Co-Evolution

• DOI: 10.1109/icdm.2019.00035
• 发表时间: 2019-11
• 期刊: 2019 IEEE International Conference on Data Mining (ICDM)
• 作者: Xiaojie Guo;Liang Zhao;Cameron Nowzari;S. Rafatirad;H. Homayoun;Sai Manoj Pudukotai Dinakarrao

Zero-Shot Cross-Lingual Machine Reading Comprehension via Inter-Sentence Dependency Graph

• DOI: 10.1609/aaai.v36i10.21407
• 发表时间: 2021-12
• 期刊: ArXiv
• 作者: Liyan Xu;Xuchao Zhang;Bo Zong;Yanchi Liu;Wei Cheng;Jingchao Ni;Haifeng Chen;Liang Zhao;Jinho D. Choi

A Systematic Survey on Deep Generative Models for Graph Generation

• DOI: 10.1109/tpami.2022.3214832
• 发表时间: 2023-05-01
• 期刊: IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
• 影响因子: 23.6
• 作者: Guo, Xiaojie;Zhao, Liang
• 通讯作者: Zhao, Liang