Collaborative Research: CIF: Medium: Robust Learning over Graphs

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。图可以为复杂的网络系统建模，因此基于图的机器学习系统旨在从结构化数据中进行推断，已经获得了很大的吸引力。另一方面，数据驱动系统必须处理噪声、不确定和外围测量，以及由不可信甚至恶意来源提供的数据。此外，图感知系统必须处理估计图结构中的错误。为了解决这些挑战，该项目引入了一个全面的框架，用于在图上进行健壮的学习，可以识别和减轻图结构中的异常值、对抗性攻击和错误。可扩展、健壮和可信学习的发展可以在社会分析、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。这个项目的首要目标是加强对图形的学习，以对抗噪声、异常、数据错误和对手。该项目由三个相互交织的推力组成：（T1）识别网络图上节点过程的异常；（T2）摄动图结构下的图感知深度学习；（T3）信息与决策融合，量化信息源的可靠性。T1利用随机抽样和共识，以及图形信号处理工具来确定外围节点。处理有噪声或扰动的图拓扑，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将设计和分析图认知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到更广泛的研究社区。该奖项反映了美国国家科学基金会的法定使命，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。

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.