III: Small: Deep Generative Models for Temporal Graph Generation and Interpretation
III:小:用于时间图生成和解释的深度生成模型
基本信息
- 批准号:2103592
- 负责人:
- 金额:$ 49.81万
- 依托单位:
- 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:
- 关键词:
项目摘要
Temporal graphs represent a crucial type of data structure where the entities and their connections evolve over time. These time-evolving phenomena are ubiquitous in real-world networks such as social networks, biological networks, and cyber networks. Existing generative models of temporal graphs typically rely on the principles of temporal process of network generation predefined by human heuristics and prior knowledge, such as temporal exponential random graphs, randomized reference models, and dynamic Bayesian models. They usually fit well towards the properties that the predefined principles are tailored for, but usually cannot do well for the others. Unfortunately, the mechanisms of many critical real-world network dynamics are still largely unknown, such as co-evolution of structural and functional connectivities in brain networks, catastrophic cascading failures in power networks, and malware epidemics in the Internet of Things. This project focuses on developing a transformative framework for temporal graphs generative modeling that can automatically learn, characterize, and interpret the underlying patterns and principles from temporal graph observation data. It aims at significantly benefiting the related scientific and engineering domains with open-sourced tools for temporal graphs modeling and network dynamics knowledge distillation. The project includes educational and engagement activities that will substantially increase the community's understanding of temporal graphs.This project will develop a generic framework of generative deep neural networks for temporal graph modeling, generation, and interpretation. The two major types of network dynamic patterns will be investigated, including "topological dynamics of a graph" (e.g., growth of a social network) and "activity dynamics on a graph" (e.g., real-time communications in contact networks). The proposed framework will: 1) automatically learn the (unknown) process of topological dynamics and activity dynamics in discrete- and continuous-time temporal graphs, 2) enforce validity constraints on dynamic topologies and time-evolving activities over the generated temporal graphs, and 3) pursue the temporal graph model interpretability for network dynamic patterns distillation and model intervention. To achieve the above research goals, a number of research activities will be conducted including: i) develop scalable deep generative models for dynamic topologies of large temporal graphs under the temporal-topological constraints, ii) propose novel deep generative models for activity dynamics in temporal graphs under validity-guarantee activation functions, iii) design strategies for modeling the co-evolution of topological dynamics and activity dynamics, iv) pursue model interpretability enhancement by disentangling static and dynamic patterns as well as post-hoc interpretation on the generated temporal graphs by dynamic graph attention and dynamic subgraph detection techniques, and v) develop a novel human-model interaction system for temporal graph visualization and model intervention.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.
时间图表示一种关键类型的数据结构,其中实体及其连接随着时间的推移而发展。这些随时间变化的现象在社会网络、生物网络和网络网络等现实世界网络中无处不在。现有的时间图生成模型通常依赖于人类启发式和先验知识预先定义的网络生成的时间过程原理,如时间指数随机图、随机参考模型和动态贝叶斯模型。它们通常很适合预定义原则所定制的属性,但通常不适用于其他属性。不幸的是,许多关键的现实世界网络动态的机制在很大程度上仍然未知,例如大脑网络中结构和功能连接的共同进化,电力网络中的灾难性级联故障,以及物联网中的恶意软件流行。该项目侧重于开发一个时间图生成建模的转换框架,该框架可以自动学习、表征和解释时间图观测数据中的潜在模式和原则。它的目的是显著地受益于相关的科学和工程领域与开源工具的时间图建模和网络动力学知识蒸馏。该项目包括教育和参与活动,将大大提高社区对时间图的理解。该项目将开发一个用于时间图建模、生成和解释的生成深度神经网络的通用框架。将研究两种主要类型的网络动态模式,包括“图的拓扑动态”(例如,社会网络的增长)和“图上的活动动态”(例如,接触网络中的实时通信)。提出的框架将:1)自动学习离散和连续时间时间图中拓扑动态和活动动态的(未知)过程;2)在生成的时间图上对动态拓扑和时间演化活动实施有效性约束;3)追求网络动态模式蒸馏和模型干预的时间图模型可解释性。为达致上述研究目标,我们将进行多项研究活动,包括:1)在时间拓扑约束下,开发大型时间图动态拓扑的可扩展深度生成模型;2)在有效性保证激活函数下,提出新的时间图活动动态深度生成模型;3)拓扑动力学和活动动力学协同演化建模策略。Iv)通过动态图注意和动态子图检测技术对生成的时间图进行静态和动态模式的分离和事后解释,提高模型的可解释性;v)开发用于时间图可视化和模型干预的新型人-模型交互系统。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
期刊论文数量(27)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Adaptive Kernel Graph Neural Network
- DOI:10.1609/aaai.v36i6.20664
- 发表时间:2021-12
- 期刊:
- 影响因子:0
- 作者:Mingxuan Ju;Shifu Hou;Yujie Fan;Jianan Zhao;Liang Zhao;Yanfang Ye
- 通讯作者:Mingxuan Ju;Shifu Hou;Yujie Fan;Jianan Zhao;Liang Zhao;Yanfang Ye
Deep Multi-attributed Graph Translation with Node-Edge Co-Evolution
- DOI:10.1109/icdm.2019.00035
- 发表时间:2019-11
- 期刊:
- 影响因子:0
- 作者:Xiaojie Guo;Liang Zhao;Cameron Nowzari;S. Rafatirad;H. Homayoun;Sai Manoj Pudukotai Dinakarrao
- 通讯作者:Xiaojie Guo;Liang Zhao;Cameron Nowzari;S. Rafatirad;H. Homayoun;Sai Manoj Pudukotai Dinakarrao
Small molecule generation via disentangled representation learning
- DOI:10.1093/bioinformatics/btac296
- 发表时间:2022-05
- 期刊:
- 影响因子:5.8
- 作者:Yuanqi Du;Xiaojie Guo;Yinkai Wang;Amarda Shehu;Liang Zhao
- 通讯作者:Yuanqi Du;Xiaojie Guo;Yinkai Wang;Amarda Shehu;Liang Zhao
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:
- 影响因子:6
- 作者:Junxiang Wang;Fuxun Yu;Xiangyi Chen;Liang Zhao
- 通讯作者:Junxiang Wang;Fuxun Yu;Xiangyi Chen;Liang Zhao
Deep Generative Model for Periodic Graphs
- DOI:
- 发表时间:2022-01
- 期刊:
- 影响因子:0
- 作者:Shiyu Wang;Xiaojie Guo;Liang Zhao
- 通讯作者:Shiyu Wang;Xiaojie Guo;Liang Zhao
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Liang Zhao其他文献
Novel imidazolium stationary phase for high-performance liquid chromatography.
用于高效液相色谱的新型咪唑固定相。
- DOI:
10.1016/j.chroma.2006.03.016 - 发表时间:
2006-05 - 期刊:
- 影响因子:0
- 作者:
Hongdeng Qiu;Shengxiang Jiang;Xia Liu*;Liang Zhao - 通讯作者:
Liang Zhao
The QUENDA-BOT: Autonomous Robot for Screw-Fixing Installation in Timber Building Construction
QUENDA-BOT:木结构建筑中用于螺钉固定安装的自主机器人
- DOI:
10.1109/case56687.2023.10260465 - 发表时间:
2023 - 期刊:
- 影响因子:0
- 作者:
Dinh Dang Khoa Le;Gibson Hu;Dikai Liu;R. Khonasty;Liang Zhao;Shoudong Huang;Pratik Shrestha;R. Belperio - 通讯作者:
R. Belperio
Phenotypic effects of the nurse Thylacospermum caespitosum on dependent plant species along regional climate stress gradients
沿区域气候胁迫梯度,袋囊草保育员对依赖植物物种的表型影响
- DOI:
10.1111/oik.04512 - 发表时间:
2018-02 - 期刊:
- 影响因子:3.4
- 作者:
Xingpei Jiang;Richard Michalet;Shuyan Chen;Liang Zhao;Xiangtai Wang;Chenyue Wang;Lizhe An;Sa Xiao - 通讯作者:
Sa Xiao
Interannual variability of dimethylsulfide in the Yellow Sea
黄海二甲硫醚的年际变化
- DOI:
10.1007/s00343-020-0480-0 - 发表时间:
2022-02 - 期刊:
- 影响因子:1.6
- 作者:
Sijia Wang;Qun Sun;Shuai Li;Jiawei Shen;Qian Liu;Liang Zhao - 通讯作者:
Liang Zhao
Tape-Assisted Photolithographic-Free Microfluidic Chip Cell Patterning for Tumor Metastasis Study
用于肿瘤转移研究的胶带辅助免光刻微流控芯片细胞图案化
- DOI:
10.1021/acs.analchem.7b03225 - 发表时间:
2017 - 期刊:
- 影响因子:7.4
- 作者:
Liang Zhao;Tengfei Guo;Lirong Wang;Yang Liu;Ganyu Chen;Hao Zhou;Meiqin Zhang - 通讯作者:
Meiqin Zhang
Liang Zhao的其他文献
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{{ truncateString('Liang Zhao', 18)}}的其他基金
Collaborative Research: OAC Core: Distributed Graph Learning Cyberinfrastructure for Large-scale Spatiotemporal Prediction
合作研究:OAC Core:用于大规模时空预测的分布式图学习网络基础设施
- 批准号:
2403312 - 财政年份:2024
- 资助金额:
$ 49.81万 - 项目类别:
Standard Grant
CAREER: Uncovering Solar Wind Composition, Acceleration, and Origin through Observations, Modeling, and Machine Learning Methods
职业:通过观测、建模和机器学习方法揭示太阳风的成分、加速度和起源
- 批准号:
2237435 - 财政年份:2023
- 资助金额:
$ 49.81万 - 项目类别:
Continuing Grant
Travel: NSF Student Travel Support for the 2023 IEEE International Conference on Data Mining (IEEE ICDM 2023)
旅行:2023 年 IEEE 国际数据挖掘会议 (IEEE ICDM 2023) 的 NSF 学生旅行支持
- 批准号:
2324784 - 财政年份:2023
- 资助金额:
$ 49.81万 - 项目类别:
Standard Grant
SHINE: Understanding the Physical Connection of the in-situ Properties and Coronal Origins of the Solar Wind with a Novel Artificial Intelligence Investigation
SHINE:通过新颖的人工智能研究了解太阳风的原位特性和日冕起源的物理联系
- 批准号:
2229138 - 财政年份:2022
- 资助金额:
$ 49.81万 - 项目类别:
Continuing Grant
III: Small: Graph Generative Deep Learning for Protein Structure Prediction
III:小:用于蛋白质结构预测的图生成深度学习
- 批准号:
2110926 - 财政年份:2020
- 资助金额:
$ 49.81万 - 项目类别:
Standard Grant
OAC Core: SMALL: DeepJIMU: Model-Parallelism Infrastructure for Large-scale Deep Learning by Gradient-Free Optimization
OAC 核心:小型:DeepJIMU:通过无梯度优化实现大规模深度学习的模型并行基础设施
- 批准号:
2007976 - 财政年份:2020
- 资助金额:
$ 49.81万 - 项目类别:
Standard Grant
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 - 财政年份:2020
- 资助金额:
$ 49.81万 - 项目类别:
Continuing Grant
CRII: III: Interpretable Models for Spatio-Temporal Event Forecasting using Social Sensors
CRII:III:使用社交传感器进行时空事件预测的可解释模型
- 批准号:
2103745 - 财政年份:2020
- 资助金额:
$ 49.81万 - 项目类别:
Standard Grant
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:
1942594 - 财政年份:2020
- 资助金额:
$ 49.81万 - 项目类别:
Continuing Grant
OAC Core: SMALL: DeepJIMU: Model-Parallelism Infrastructure for Large-scale Deep Learning by Gradient-Free Optimization
OAC 核心:小型:DeepJIMU:通过无梯度优化实现大规模深度学习的模型并行基础设施
- 批准号:
2106446 - 财政年份:2020
- 资助金额:
$ 49.81万 - 项目类别:
Standard Grant
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