TRIPODS: Towards a Unified Theory of Structure, Incompleteness & Uncertainty in Heterogeneous Graphs

TRIPODS：迈向结构、不完备性的统一理论

• 批准号: 1740850
• 负责人: Lise Getoor
• 金额: $ 150万
• 依托单位: University of California-Santa Cruz
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-01 至 2022-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1740850&HistoricalAwards=false
• 关键词: TRIPODS; Towards; Unified; Theory; Structure

This project brings together researchers from mathematics, statistics, and computer science to develop a unified theory of data science applied to uncertain and heterogeneous graph and network data. Most real-world applications of networks involve complex phenomena, such as socio-behavioral interactions, biological and/or chemical processes, technical systems like data centers, and communication systems for smart cities. These data are heterogeneous, including multiple modalities and multiple scales. Crucially, the data observed is often incomplete and very noisy. A new foundation for data science needs to be built in order to address these challenges in the context of graph and network data. Similarly, we lack a clear unified theory that allows us to understand how to quantify the uncertainty in the system that arises from the uncertainty in the relationships among its actors. This is a fertile area for transdisciplinary collaboration between statisticians, mathematicians, and computer scientists, with strong impacts on industry, academia, government and broader society. This project centers around two research themes. In the first theme, the PIs will investigate models of algorithms on uncertain network data, and specifically combine techniques from sub-linear algorithms with Bayesian methods. The second theme focuses on how algorithms can benefit from data uncertainty, in the context of privacy, disclosure, and robustness to noise. In both these themes, technical advances will be achieved by marrying computational approaches to uncertainty with statistical and mathematical approaches for uncertainty. In addition to the research agenda, the project involves an ambitious vision for data science capabilities spanning academia to industry. The education aspects of this vision include a series of themed workshops and the development of comprehensive educational resources spanning secondary, undergraduate and advanced graduate materials. The project also involves collaboration between UC Santa Cruz and Silicon Valley companies that will ground our proposed theoretical and algorithmic advances with practical applications to real-world problems and data. Furthermore, these collaborations with industrial partners will lead to specialized workforce development. Looking forward towards Phase II, the project aims to develop collaborations with industry partners and various academic institutions in the area to develop a Silicon Valley/Greater Bay Area Institute on Foundations of Data Science, potentially to be located at University of California Santa Cruz Silicon Valley campus in Santa Clara. Funds for the project come from CISE Information Technology Research and MPS Division of Mathematical Sciences.

该项目汇集了来自数学、统计学和计算机科学的研究人员，以开发适用于不确定和异质图形和网络数据的统一数据科学理论。大多数现实世界中的网络应用都涉及复杂的现象，例如社会行为交互、生物和/或化学过程、数据中心等技术系统以及智能城市的通信系统。这些数据是异质的，包括多个模式和多个尺度。至关重要的是，观察到的数据往往是不完整的，而且噪音很大。需要为数据科学建立一个新的基础，以便在图形和网络数据的背景下解决这些挑战。同样，我们缺乏一个明确的统一理论，使我们能够理解如何量化系统中因其行为者之间关系的不确定性而产生的不确定性。这是统计学家、数学家和计算机科学家之间进行跨学科合作的肥沃领域，对工业界、学术界、政府和更广泛的社会产生了巨大影响。该项目围绕两个研究主题展开。在第一个主题中，PI将研究不确定网络数据的算法模型，并具体将次线性算法的技术与贝叶斯方法相结合。第二个主题集中于算法如何在隐私、披露和对噪声的健壮性的背景下从数据不确定性中受益。在这两个主题中，通过将处理不确定性的计算方法与处理不确定性的统计和数学方法结合起来，将实现技术进步。除了研究议程，该项目还涉及从学术界到工业界的数据科学能力的雄心勃勃的愿景。这一愿景的教育方面包括一系列主题讲习班和开发涵盖中学、本科和高级研究生材料的综合教育资源。该项目还涉及加州大学圣克鲁斯分校和硅谷公司之间的合作，这将为我们提出的理论和算法进步奠定基础，并将其实际应用于现实世界的问题和数据。此外，与行业合作伙伴的这些合作将导致专业化的劳动力发展。展望第二阶段，该项目旨在与该地区的行业合作伙伴和各种学术机构开展合作，建立一个硅谷/大湾区数据科学基础研究所，可能设在圣克拉拉的加州大学圣克鲁斯分校硅谷校区。该项目的资金来自中科院信息技术研究所和MPS数学科学部。

项目成果

Estimating Causal Effects of Tone in Online Debates

估计在线辩论中语气的因果效应

• DOI: 10.24963/ijcai.2019/259
• 发表时间: 2019
• 期刊: International Joint Conference on Artificial Intelligence
• 作者: Sridhar, Dhanya;Getoor, Lise
• 通讯作者: Getoor, Lise

A Declarative Approach to Fairness in Relational Domains

关系领域公平性的声明性方法

• 发表时间: 2019
• 期刊: Data engineering
• 作者: Farnadi, G;Babaki, B;Getoor, L
• 通讯作者: Getoor, L

Estimating Aggregate Properties In Relational Networks With Unobserved Data

• 发表时间: 2020-01
• 期刊: ArXiv
• 作者: Varun R. Embar;S. Srinivasan;L. Getoor

Decoupled Smoothing in Probabilistic Soft Logic

• 发表时间: 2020
• 作者: Yatong Chen

Random walks and forbidden minors II: a poly( d ε -1 )-query tester for minor-closed properties of bounded degree graphs

随机游走和禁止未成年人 II：有界度图的未成年人封闭性质的 Poly( d ε -1 ) 查询测试器

• DOI: 10.1145/3313276.3316330
• 发表时间: 2019
• 期刊: Symposium on Theory of Computing (STOC
• 作者: Kumar, Akash;Seshadhri, C.;Stolman, Andrew
• 通讯作者: Stolman, Andrew