CAREER: New Frontiers in Graph Generation

职业:图生成的新领域

基本信息

  • 批准号:
    2239869
  • 负责人:
  • 金额:
    $ 55.64万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
    Continuing Grant
  • 财政年份:
    2023
  • 资助国家:
    美国
  • 起止时间:
    2023-04-01 至 2028-03-31
  • 项目状态:
    未结题

项目摘要

A graph is a model of many different types of connections, relationships, or networks between different entities. It can be used to represent micro-level objects, like molecules being recorded as bonds connecting atoms, and macro-level networks, like social networks consisting of connections between users. Graph structures often contain rich information and are large in scale. An important task is to synthesize new graphs that are similar to but different from existing ones. For example, a drug design task may require a model to generate new molecule graphs for screening; and a data-sharing task for a social network may need a model to synthesize and share a graph similar to the original network, without releasing sensitive link information. This project combines neural networks and probabilistic methods to develop tools for generating new graphs for a wide range of tasks. These tools also have a solid statistical foundation and help to deepen the understanding of graph data.This project will advocate a new direction of developing graph generative models based on discrete sequential processes—the generative model starts from a random or trivial graph and tailors it in multiple steps to generate a random graph. The research effort in this project has three technical aims. First, the project will develop a probabilistic framework for building graph generative models with neural networks. Second, the project will overcome efficiency issues in model training and model predictions. Third, the project will compare newly developed models with traditional random graph models to deepen the understanding of network data. From the comparison, new methods will also be developed to preserve private information in the sharing of network data. Models to be developed from this project will have a solid statistical foundation and be connected to traditional random graph models.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.
图是不同实体之间的许多不同类型的连接、关系或网络的模型。它可以用来表示微观层次的对象,如分子被记录为连接原子的键,以及宏观层次的网络,如由用户之间的连接组成的社交网络。图结构通常包含丰富的信息,并且规模很大。一个重要的任务是合成新的图,是类似的,但不同于现有的。例如,药物设计任务可能需要一个模型来生成新的分子图以进行筛选;社交网络的数据共享任务可能需要一个模型来合成和共享与原始网络相似的图,而不会释放敏感的链接信息。该项目结合了神经网络和概率方法,开发用于为各种任务生成新图形的工具。本项目将倡导一个基于离散顺序过程的图生成模型开发的新方向--生成模型从一个随机或平凡的图开始,经过多步裁剪生成一个随机图。该项目的研究工作有三个技术目标。首先,该项目将开发一个概率框架,用于使用神经网络构建图生成模型。其次,该项目将克服模型训练和模型预测中的效率问题。第三,该项目将比较新开发的模型与传统的随机图模型,以加深对网络数据的理解。通过比较,还将开发新的方法来保护网络数据共享中的隐私信息。该项目开发的模型将具有坚实的统计基础,并与传统的随机图模型相连接。该奖项反映了NSF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(7)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
On Separate Normalization in Self-supervised Transformers
  • DOI:
    10.48550/arxiv.2309.12931
  • 发表时间:
    2023-09
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Xiaohui Chen;Yinkai Wang;Yuanqi Du;S. Hassoun;Liping Liu
  • 通讯作者:
    Xiaohui Chen;Yinkai Wang;Yuanqi Du;S. Hassoun;Liping Liu
Fitting Autoregressive Graph Generative Models through Maximum Likelihood Estimation
  • DOI:
  • 发表时间:
    2023
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Xuhong Han;Xiaohui Chen;Francisco J. R. Ruiz;Liping Liu
  • 通讯作者:
    Xuhong Han;Xiaohui Chen;Francisco J. R. Ruiz;Liping Liu
Efficient and Degree-Guided Graph Generation via Discrete Diffusion Modeling
  • DOI:
    10.48550/arxiv.2305.04111
  • 发表时间:
    2023-05
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Xiaohui Chen;Jiaxing He;Xuhong Han;Liping Liu
  • 通讯作者:
    Xiaohui Chen;Jiaxing He;Xuhong Han;Liping Liu
Unifying Predictions of Deterministic and Stochastic Physics in Mesh-reduced Space with Sequential Flow Generative Model
用顺序流生成模型统一网格缩减空间中确定性和随机物理的预测
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Liping Liu其他文献

High‐grade endometrial stromal sarcoma of the endocervix: An extremely rare case report
宫颈内膜高级别子宫内膜间质肉瘤:极其罕见的病例报告
Oxidized low-density lipoprotein predicts recurrent stroke in patients with minor stroke or TIA
氧化低密度脂蛋白可预测轻微中风或 TIA 患者的复发性中风
  • DOI:
  • 发表时间:
    2018
  • 期刊:
  • 影响因子:
    9.9
  • 作者:
    Anxin Wang;Jie Xu;Guojuan Chen;David Wang;S. C. Johnston;X. Meng;Jinxi Lin;Hao Li;Yibin Cao;Nan Zhang;Caiyun Ma;L. Dai;Xingquan Zhao;Liping Liu;Yongjun Wang;Yilong Wang
  • 通讯作者:
    Yilong Wang
Comparison Study on Short Circuit Capability of 1.2 kV Split-Gate MOSFET and Split-Source MOSFET with Integrated JBS Diode
1.2 kV分栅MOSFET与集成JBS二极管的分源MOSFET短路能力对比研究
Autoimmune Encephalomyelitis Mice with Experimental − / − CXCR 3 Production in γ Migration , and Reduced IFN-Severe Disease , Unaltered Leukocyte
自身免疫性脑脊髓炎小鼠实验性 - / - γ 迁移中 CXCR 3 的产生,IFN-严重疾病减少,白细胞未改变
  • DOI:
  • 发表时间:
    2006
  • 期刊:
  • 影响因子:
    0
  • 作者:
    R. Ransohoff;Taofang Hu;J. DeMartino;B. Lu;C. Gerard;Liping Liu;Deren Huang;M. Matsui;Toby T. He
  • 通讯作者:
    Toby T. He
Optical and Geometrical Properties of Cirrus Clouds over the Tibetan Plateau Measured by Lidar and Radiosonde Sounding at the Summertime in 2014
2014年夏季激光雷达和无线电探空仪测量青藏高原卷云的光学和几何特性

Liping Liu的其他文献

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{{ truncateString('Liping Liu', 18)}}的其他基金

Anomalous Diffusion: Physical Origins and Mathematical Analysis
反常扩散:物理起源和数学分析
  • 批准号:
    2306254
  • 财政年份:
    2023
  • 资助金额:
    $ 55.64万
  • 项目类别:
    Continuing Grant
CISE: RI: Small: Amortized Inference for Large-Scale Graphical Models
CISE:RI:小型:大规模图形模型的摊销推理
  • 批准号:
    1908617
  • 财政年份:
    2019
  • 资助金额:
    $ 55.64万
  • 项目类别:
    Standard Grant
CRII: RI: Self-Attention through the Bayesian Lens
CRII:RI:贝叶斯视角下的自注意力
  • 批准号:
    1850358
  • 财政年份:
    2019
  • 资助金额:
    $ 55.64万
  • 项目类别:
    Standard Grant
Polynomial inclusions: open problems and potential applications
多项式包含:开放问题和潜在应用
  • 批准号:
    1410273
  • 财政年份:
    2014
  • 资助金额:
    $ 55.64万
  • 项目类别:
    Standard Grant
CAREER: Multiferroic Materials - Predictive Modeling, Multiscale Analysis, and Optimal Design
职业:多铁性材料 - 预测建模、多尺度分析和优化设计
  • 批准号:
    1351561
  • 财政年份:
    2014
  • 资助金额:
    $ 55.64万
  • 项目类别:
    Standard Grant
Variational Inequalities and their Applications in the Predictive Modeling of Heterogeneous Media
变分不等式及其在异质介质预测建模中的应用
  • 批准号:
    1238835
  • 财政年份:
    2012
  • 资助金额:
    $ 55.64万
  • 项目类别:
    Standard Grant
Variational Inequalities and their Applications in the Predictive Modeling of Heterogeneous Media
变分不等式及其在异质介质预测建模中的应用
  • 批准号:
    1101030
  • 财政年份:
    2011
  • 资助金额:
    $ 55.64万
  • 项目类别:
    Standard Grant

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