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.

该项目汇集了数学，统计和计算机科学的研究人员，以开发出一种统一的数据科学理论，该理论应用于不确定和异构图和网络数据。 网络的大多数现实应用都涉及复杂的现象，例如社会行为相互作用，生物学和/或化学过程，诸如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，包括多种模态和多个量表。至关重要的是，观察到的数据通常不完整且非常嘈杂。 需要建立一个新的数据科学基础，以便在图形和网络数据的背景下解决这些挑战。 同样，我们缺乏明确的统一理论，该理论使我们能够理解如何量化系统中的不确定性的不确定性。 这是统计学家，数学家和计算机科学家之间跨学科合作的肥沃领域，对工业，学术界，政府和更广泛的社会产生了强烈影响。该项目围绕两个研究主题。在第一个主题中，PI将研究不确定的网络数据上的算法模型，并特别结合了子线性算法的技术和贝叶斯方法。 第二个主题侧重于算法如何在隐私，披露和噪声鲁棒性的背景下从数据不确定性中受益。在这两个主题中，都将通过将计算方法与统计和数学方法结合到不确定性来实现技术进步。 除研究议程外，该项目还涉及跨越学术界的数据科学能力的雄心勃勃的愿景。该愿景的教育方面包括一系列主题研讨会以及跨越二级，本科和高级研究生材料的综合教育资源的发展。该项目还涉及加州大学圣克鲁斯分校与硅谷公司之间的合作，这些公司将基础我们提出的理论和算法进步，并在现实世界中的问题和数据上采用实际应用。此外，这些与工业合作伙伴的合作将导致专门的劳动力发展。 期待第二阶段，该项目旨在与该地区的行业合作伙伴和各个学术机构建立合作，以开发硅谷/大湾地区研究所的数据科学基础，可能位于加利福尼亚大学圣克鲁斯硅谷分校的圣克鲁斯硅谷校园。 该项目的资金来自CISE信息技术研究和数学科学的国会议员部门。

项目成果

Decoupled Smoothing in Probabilistic Soft Logic

• 发表时间: 2020
• 作者: Yatong Chen

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

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

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