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Collaborative Research: CIF: Medium: Robust Learning over Graphs

协作研究：CIF：媒介：图上的鲁棒学习

基本信息

批准号：
2312547
负责人：
Georgios Giannakis
金额：
$ 35.67万
依托单位：
University of Minnesota-Twin Cities
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：
2023-06-01 至 2026-05-31
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2312547&HistoricalAwards=false
关键词：
Collaborative Research CIF Medium Robust

项目摘要

Connected sensors, autonomous systems and the Internet all produce vast amounts of structured data that must be analyzed. Graphs can model complex networked systems, and as such graph-based machine learning systems aiming at inferences from structured data, have gained significant traction. Data-driven systems, on the other hand, must deal with noisy, uncertain, and outlying measurements, as well as data provided by untrustworthy or even malicious sources. In addition, graph-aware systems must cope with errors in the estimated graph structure. Aspiring to address these challenges, this project introduces a comprehensive framework for robust learning over graphs that can identify and mitigate outliers, adversarial attacks, and errors in the graph structure. The development of scalable, robust, and trustworthy learning can enable active and timely inference and decision-making in domains such as social analytics, crowdsourcing, health informatics, and Internet-of-Things security.The overarching goal of this project is to fortify learning over graphs against noise, anomalies, errors in the data, and adversaries. This project consists of three intertwined thrusts: (T1) identifying anomalies in nodal processes over network graphs; (T2) Graph-aware deep learning under perturbed graph structure, and; (T3) information and decision fusion to quantify the reliability of information sources. T1 leverages random sampling and consensus, as well as graph signal processing tools to pinpoint outlying nodes. Dealing with noisy or perturbed graph topologies, T2 endows graph convolutional networks with dithering-inspired modules that are robust to changes in graph links. Finally, T3 will design and analyze graph-cognizant unsupervised ensemble learning and crowdsourcing algorithms to assess the reliability and usefulness of various information sources. Results from this project will be disseminated to the broader research community through publications, workshops, and code sharing.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.

连接的传感器、自主系统和互联网都会产生大量必须分析的结构化数据。图可以对复杂的网络系统进行建模，因此基于图的机器学习系统旨在从结构化数据中进行推理，已经获得了显着的牵引力。另一方面，数据驱动的系统必须处理噪声、不确定性和外围测量，以及由不可信甚至恶意来源提供的数据。此外，图形感知系统必须科普估计的图形结构中的错误。为了应对这些挑战，该项目引入了一个全面的框架，用于在图上进行鲁棒学习，可以识别和减轻图结构中的离群值，对抗性攻击和错误。开发可扩展、健壮和可信的学习可以在社交分析、众包、健康信息学和物联网安全等领域实现主动和及时的推理和决策。该项目的总体目标是加强对数据中的噪声、异常、错误和对手的学习。该项目包括三个相互交织的重点：（T1）识别网络图上节点过程中的异常;（T2）扰动图结构下的图感知深度学习;（T3）信息和决策融合，以量化信息源的可靠性。T1利用随机采样和共识以及图形信号处理工具来精确定位外围节点。处理噪声或扰动图拓扑，T2赋予图卷积网络抖动启发模块，对图链接的变化具有鲁棒性。最后，T3将设计和分析图形认知的无监督集成学习和众包算法，以评估各种信息源的可靠性和有用性。该项目的成果将通过出版物、研讨会和代码共享传播给更广泛的研究社区。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（4）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Robust Identification of Graph Structure

图结构的鲁棒识别

DOI：
10.1109/camsap58249.2023.10403439
发表时间：
2023
期刊：
IEEE
影响因子：
0
作者：
Karanikolas, Georgios V.;Giannakis, Georgios B.
通讯作者：
Giannakis, Georgios B.

Identifying Dependent Annotators in Crowdsourcing

识别众包中的依赖注释器

DOI：
10.1109/ieeeconf56349.2022.10052052
发表时间：
2022
期刊：
Asilomar Conference on Signals Systems and Computers
影响因子：
0
作者：
Traganitis, Panagiotis A.;Giannakis, Georgios B.
通讯作者：
Giannakis, Georgios B.

Bayesian Optimization for Online Management in Dynamic Mobile Edge Computing

DOI：
10.1109/twc.2023.3307875
发表时间：
2022-11
期刊：
IEEE Transactions on Wireless Communications
影响因子：
10.4
作者：
Jiahe Yan;Qin Lu;G. Giannakis
通讯作者：
Jiahe Yan;Qin Lu;G. Giannakis

Bayesian Self-Supervised Learning Using Local and Global Graph Information

DOI：
10.1109/camsap58249.2023.10403487
发表时间：
2023-12
期刊：
2023 IEEE 9th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)
影响因子：
0
作者：
Konstantinos D. Polyzos;A. Sadeghi;G. Giannakis
通讯作者：
Konstantinos D. Polyzos;A. Sadeghi;G. Giannakis