Collaborative Research: CIF: Medium: Robust Learning over Graphs

互联的传感器、自主系统和互联网都产生了大量的结构化数据，必须进行分析。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理，已经获得了显著的吸引力。另一方面，数据驱动的系统必须处理噪声、不确定和离奇的测量，以及由不可信甚至恶意来源提供的数据。此外，图形感知系统必须处理估计的图形结构中的错误。为了应对这些挑战，该项目引入了一个关于图的稳健学习的综合框架，可以识别和减少图结构中的离群值、对抗性攻击和错误。发展可扩展、稳健和可信的学习可以在社会分析、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。该项目的总体目标是加强基于图形的学习，以对抗噪声、异常、数据错误和对手。该项目包括三个相互交织的任务：(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利用随机抽样和共识以及图形信号处理工具来精确定位外围节点。在处理噪声或扰动的图拓扑时，T2赋予图卷积网络以抖动启发的模块，这些模块对图链接的变化具有健壮性。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。该项目的结果将通过出版物、研讨会和代码共享向更广泛的研究社区传播。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势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.