Collaborative Research: SAI-R: Dynamical Coupling of Physical and Social Infrastructures: Evaluating the Impacts of Social Capital on Access to Safe Well Water

合作研究：SAI-R：物理和社会基础设施的动态耦合：评估社会资本对获得安全井水的影响

基本信息

批准号：
2228534
负责人：
Jundong Li
金额：
$ 25万
依托单位：
University of Virginia Main Campus
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2022
资助国家：
美国
起止时间：
2022-09-15 至 2025-08-31
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2228534&HistoricalAwards=false
关键词：
Collaborative Research SAI Dynamical Coupling

项目摘要

Strengthening American Infrastructure (SAI) is an NSF Program seeking to stimulate human-centered fundamental and potentially transformative research that strengthens America’s infrastructure. Effective infrastructure provides a strong foundation for socioeconomic vitality and broad quality of life improvement. Strong, reliable, and effective infrastructure spurs private-sector innovation, grows the economy, creates jobs, makes public-sector service provision more efficient, strengthens communities, promotes equal opportunity, protects the natural environment, enhances national security, and fuels American leadership. To achieve these goals requires expertise from across the science and engineering disciplines. SAI focuses on how knowledge of human reasoning and decision-making, governance, and social and cultural processes enables the building and maintenance of effective infrastructure that improves lives and society and builds on advances in technology and engineering.Access to a safe supply of drinking water is essential for the health and welfare of all people. In many places, private wells are the primary source of water for residents. This SAI research project examines the availability of potable drinking water to individuals and households in settings where private wells are the predominant source of water for residents. Maintaining a safe supply of drinking water may be particularly challenging for residents who lack broad access to social support, as reflected in geographic connections to other communities. This support may be especially important in the aftermath of natural disasters and related hazards that disrupt water supplies. This project uses data on the mobility of cell phone users to characterize the social assistance that residents call upon. Methods are used to account for unequal representation of different groups in such datasets. The analysis considers other variables that may cause variation in water quality, such as demographic and socioeconomic factors. Water quality is evaluated with samples of private wells and surveys with owners. The project places high priority on sharing important findings with stakeholders, including extension services and health departments. The project also contributes to middle and high school curricula that will be shared and used in diverse public school settings.Multiple, complementary datasets are leveraged to examine the ways in which advantageous positions in social networks may contribute to better water quality in private wells, particularly in geographic settings that have been impacted by recent flooding. Social networks are constructed from data on the mobility of cellular phone users, and new algorithmic approaches are developed to address the biases that typify these data. Upon constructing these networks, measures of positions in social networks are used to predict variation in the contamination of private wells. The algorithmic approaches developed for graph neural network analysis will have broader potential applications in similar research that seeks to account for biases in the representativeness of large archival datasets, including biases that disadvantage vulnerable populations. The project involves multiple students, contributing to the training and education of early-career scientists.This award is supported by the Directorate for Social, Behavioral, and Economic (SBE) Sciences and the Directorate for Geosciences.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.

加强美国基础设施 (SAI) 是一项 NSF 计划，旨在促进以人为本的基础性和潜在变革性研究，加强美国的基础设施，为社会经济活力和广泛的生活质量改善提供坚实的基础。私营部门创新、发展经济、创造就业机会、提供更多公共部门服务、加强社区、促进平等机会、保护自然环境、增强国家安全并增强美国的领导力。要实现这些目标，需要来自各个领域的专业知识。 SAI 侧重于人类推理和决策、治理以及社会和文化过程的知识如何能够建设和维护有效的基础设施，从而改善生活和社会，并以技术和工程的进步为基础。安全的饮用水供应对于所有人的健康和福祉至关重要在许多地方，私人水井是居民的主要水源。私人水井是主要水源对于缺乏广泛社会支持的居民来说，维持安全的饮用水供应可能尤其具有挑战性，这反映在与其他社区的地理联系上，这种支持在自然灾害和相关灾害发生后可能尤其重要。该项目使用手机用户的流动性数据来描述居民所要求的社会援助，该分析考虑了可能导致差异的其他变量。水质，例如人口和社会经济因素。该项目高度重视与利益相关者（包括推广服务部门和卫生部门）分享重要调查结果，以评估水质。利用多个互补的数据集来研究社交网络中的优势地位如何有助于改善私人水井的水质，特别是在受最近洪水影响的地理环境中社交网络是根据数据构建的。关于手机的移动性用户，并开发新的算法方法来解决这些数据的典型偏差。在构建这些网络时，社交网络中的位置测量用于预测私人井污染的变化。在类似的研究中具有更广泛的潜在应用，旨在解释大型档案数据集代表性的偏差，包括对弱势群体不利的偏差。该项目涉及多名学生，有助于早期职业科学家的培训和教育。该奖项得到支持。由社会、行为和经济 (SBE) 科学理事会和地球科学理事会。该奖项反映了 NSF 的法定使命，并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，被认为值得支持。

项目成果

期刊论文数量（16）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Interpreting Unfairness in Graph Neural Networks via Training Node Attribution

通过训练节点归因解释图神经网络中的不公平性

DOI：
10.1609/aaai.v37i6.25905
发表时间：
2023-06
期刊：
Proceedings of the AAAI Conference on Artificial Intelligence
影响因子：
0
作者：
Dong, Yushun;Wang, Song;Ma, Jing;Liu, Ninghao;Li, Jundong
通讯作者：
Li, Jundong

Graph Few-shot Learning with Task-specific Structures

具有特定任务结构的图小样本学习

DOI：
发表时间：
2022-01
期刊：
Advances in neural information processing systems
影响因子：
0
作者：
Wang, Song;Chen, Chen;Li, Jundong
通讯作者：
Li, Jundong

Few-shot Node Classification with Extremely Weak Supervision

监督极弱的少样本节点分类

DOI：
10.1145/3539597.3570435
发表时间：
2023-01-06
期刊：
Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining
影响因子：
0
作者：
Song Wang;Yushun Dong;Kaize Ding;Chen Chen;Jundong Li
通讯作者：
Jundong Li

Learning Hierarchical Task Structures for Few-shot Graph Classification

学习用于小样本图分类的分层任务结构

DOI：
10.1145/3635473
发表时间：
2024-04
期刊：
ACM Transactions on Knowledge Discovery from Data
影响因子：
3.6
作者：
Wang, Song;Dong, Yushun;Huang, Xiao;Chen, Chen;Li, Jundong
通讯作者：
Li, Jundong

Path-Specific Counterfactual Fairness for Recommender Systems

推荐系统的特定路径反事实公平性

DOI：
10.1145/3580305.3599462
发表时间：
2023-08
期刊：
Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
影响因子：
0
作者：
Zhu, Yaochen;Ma, Jing;Wu, Liang;Guo, Qi;Hong, Liangjie;Li, Jundong
通讯作者：
Li, Jundong