NSF-CSIRO: Towards Interpretable and Responsible Graph Modeling for Dynamic Systems
NSF-CSIRO:迈向动态系统的可解释和负责任的图形建模
基本信息
- 批准号:2302786
- 负责人:
- 金额:$ 60万
- 依托单位:
- 依托单位国家:美国
- 项目类别:Standard Grant
- 财政年份:2023
- 资助国家:美国
- 起止时间:2023-05-15 至 2026-04-30
- 项目状态:未结题
- 来源:
- 关键词:
项目摘要
Real-world natural and engineered systems (e.g., food web, power grids, river networks, and ocean current networks) are inherently complicated and are driven by many factors with dependency relationships. Graphs have been commonly used to represent the structure and content of these systems for event prediction and risk estimation. To date, many graph learning methods, such as graph neural networks, have been proposed, but primarily for static graphs. In dynamic systems, the structure and content are simultaneously evolving in response to emerging trends and events, making it difficult to understand and interpret how each part of the graph functions in forming reliable models for predictions. This project strives to build a graph learning and interpretation framework for dynamic systems by combining sensor pattern discovery, node interaction and network functionality analysis, and physics- and knowledge-informed learning. The project will propose new algorithms for modeling and understanding large-scale dynamic systems using graphs, as well as develop a prototype for domain experts to analyze their data, explain what is currently happening in the system, understand the resulting consequences, and provide possible mitigation strategies. The joint effort between the US and Australian teams will help understand/uncover the dynamics of water monitoring systems for different terrain types, inland and coastal water exchange, toxic algal blooms, and resilience of rural and regional communities.The project includes three main thrusts: (1) sensor signal to feature extraction and understanding; (2) dynamic network node modeling and interpretation; and (3) dynamic network functionality and trustworthiness. The research will study signal snippet pattern (SSP) extraction and interaction analysis to understand how features interact with each other during the emergence of significant events. At the node level, new temporal encoding and spatial-temporal graph neural networks will be used to learn models for node event prediction and anomaly detection for early warning. The study of node interaction will answer why, when, and how two nodes may be interacting with each other. Beyond node level interpretation, the project will target graph functional units, estimate each snapshot graph’s contribution, and locate subgraphs with the highest significance concerning output systems. A perturbation-based post-hoc explainer will provide counterfactual explanations to enhance the explainability and trustworthiness of dynamic graph neural network systems. The research will also investigate combining physics laws and domain knowledge into dynamic graph neural networks to develop a data-efficient, robust, and responsible graph modeling framework. This is a joint project between U.S. and Australian researchers funded by the Collaboration Opportunities in Responsible and Equitable AI under the U.S. NSF and the Australian Commonwealth Scientific and Industrial Research Organisation (CSIRO).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.
现实世界中的自然和工程系统(如食物网、电网、河流网络和洋流网络)本质上是复杂的,由许多具有依赖关系的因素驱动。图表通常被用来表示这些系统的结构和内容,用于事件预测和风险估计。到目前为止,已经提出了许多图学习方法,如图神经网络,但主要针对静态图。在动态系统中,结构和内容随着新出现的趋势和事件而同时演变,这使得很难理解和解释图形的每个部分如何在形成可靠的预测模型方面发挥作用。该项目致力于通过结合传感器模式发现、节点交互和网络功能分析以及物理和知识知识学习来构建动态系统的图形学习和解释框架。该项目将提出新的算法,用于使用图形来建模和理解大规模动态系统,以及开发一个原型,供领域专家分析他们的数据,解释系统中当前发生的事情,了解由此产生的后果,并提供可能的缓解策略。美国和澳大利亚团队的合作将有助于了解/发现针对不同地形类型、内陆和沿海水域交换、有毒藻华以及农村和地区社区恢复能力的水监测系统的动态。该项目包括三个主要方面:(1)传感器信号到特征提取和理解;(2)动态网络节点建模和解释;以及(3)动态网络功能和可信性。这项研究将研究信号片段模式(SSP)的提取和交互分析,以了解在重大事件出现时特征如何相互作用。在节点级,将使用新的时间编码和时空图神经网络来学习节点事件预测和异常检测的预警模型。节点相互作用的研究将回答两个节点为什么、何时以及如何相互作用。除了节点级别的解释,该项目还将以图形功能单元为目标,估计每个快照图形的贡献,并定位与输出系统相关的最高重要性的子图。基于扰动的后自组织解释器将提供反事实解释,以增强动态图神经网络系统的可解释性和可信性。该研究还将探索将物理定律和领域知识结合到动态图神经网络中,以开发一个数据高效、健壮和负责任的图建模框架。这是美国和澳大利亚研究人员的联合项目,由美国国家科学基金会和澳大利亚联邦科学与工业研究组织(CSIRO)下的负责任和公平人工智能合作机会资助。该奖项反映了NSF的法定使命,并通过使用基金会的智力优势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(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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Xingquan Zhu其他文献
Self-adaptive attribute weighting for Naive Bayes classification
朴素贝叶斯分类的自适应属性加权
- DOI:
10.1016/j.eswa.2014.09.019 - 发表时间:
2015-02 - 期刊:
- 影响因子:8.5
- 作者:
Jia Wu;Shirui Pan;Xingquan Zhu;Zhihua Cai;Peng Zhang;Chengqi Zhang - 通讯作者:
Chengqi Zhang
Screening for different genotypes of Echinococcus granulosus within China and Argentina by single-strand conformation polymorphism (SSCP) analysis.
通过单链构象多态性(SSCP)分析筛选中国和阿根廷细粒棘球绦虫的不同基因型。
- DOI:
- 发表时间:
1999 - 期刊:
- 影响因子:2.2
- 作者:
Lihua Zhang;Robin B. Gasser;Xingquan Zhu;Donald P. McManus - 通讯作者:
Donald P. McManus
VoB predictors: Voting on bagging classifications
VoB 预测器:对 bagging 分类进行投票
- DOI:
- 发表时间:
2008 - 期刊:
- 影响因子:0
- 作者:
Xiaoyuan Su;T. Khoshgoftaar;Xingquan Zhu - 通讯作者:
Xingquan Zhu
Co-occurring evidence discovery for COPD patients using natural language processing
使用自然语言处理发现慢性阻塞性肺病患者的同时发生的证据
- DOI:
- 发表时间:
2017 - 期刊:
- 影响因子:0
- 作者:
Christopher Baechle;Ankur Agarwal;Ravi S. Behara;Xingquan Zhu - 通讯作者:
Xingquan Zhu
Automated graph anomaly detection with large language models
使用大型语言模型的自动化图形异常检测
- DOI:
10.1016/j.knosys.2025.113809 - 发表时间:
2025-08-03 - 期刊:
- 影响因子:7.600
- 作者:
Jiaqi Yu;Yang Gao;Hong Yang;Zhihong Tian;Peng Zhang;Xingquan Zhu - 通讯作者:
Xingquan Zhu
Xingquan Zhu的其他文献
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{{ truncateString('Xingquan Zhu', 18)}}的其他基金
Collaborative Research: III: Small: Taming Large-Scale Streaming Graphs in an Open World
协作研究:III:小型:在开放世界中驯服大规模流图
- 批准号:
2236579 - 财政年份:2023
- 资助金额:
$ 60万 - 项目类别:
Standard Grant
NSF Student Travel Support for the 2022 IEEE International Conference on Data Mining (IEEE ICDM 2022)
NSF 学生参加 2022 年 IEEE 国际数据挖掘会议 (IEEE ICDM 2022) 的旅行支持
- 批准号:
2226627 - 财政年份:2022
- 资助金额:
$ 60万 - 项目类别:
Standard Grant
NSF Student Travel Grant for the 2021 IEEE International Conference on Big Data (IEEE BigData 2021)
2021 年 IEEE 国际大数据会议 (IEEE BigData 2021) 的 NSF 学生旅费补助金
- 批准号:
2129417 - 财政年份:2021
- 资助金额:
$ 60万 - 项目类别:
Standard Grant
RAPID: COVID-19 Coronavirus Testbed and Knowledge Base Construction and Personalized Risk Evaluation
RAPID:COVID-19冠状病毒测试平台和知识库建设以及个性化风险评估
- 批准号:
2027339 - 财政年份:2020
- 资助金额:
$ 60万 - 项目类别:
Standard Grant
MRI: Acquisition of Artificial Intelligence & Deep Learning (AIDL) Training and Research Laboratory
MRI:人工智能的获取
- 批准号:
1828181 - 财政年份:2018
- 资助金额:
$ 60万 - 项目类别:
Standard Grant
III: Medium: Collaborative Research: KMELIN: Knowledge Mining and Embedding Learning for Complex Dynamic Information Networks
III:媒介:协作研究:KMELIN:复杂动态信息网络的知识挖掘和嵌入学习
- 批准号:
1763452 - 财政年份:2018
- 资助金额:
$ 60万 - 项目类别:
Continuing Grant
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