III: SMALL: Graph Contrastive Learning for Few-Shot Node Classification

图是由节点和边组成的数据结构。图数据是与图中的节点和边相关联的数据。图数据是巨大的，并且广泛存在于许多实际应用中。社交媒体数据是典型的图数据，其中用户是节点，他们之间的关系是边。由于用户有不同的配置文件，他们可以在自己之间形成不同的关系（即边缘）。当数据集很大时，用基础事实对其进行注释或标记是耗时和劳动密集型的。解决标记数据稀缺问题是机器学习和数据挖掘有效处理图数据等大数据的迫切需要。当我们只能标记少量的数据时，我们能从大的图形数据中学习吗？图少射节点分类是研究人员努力寻找新解决方案的一个问题，在这种分类中，只有少量数据被标记时才能进行学习。在这样的问题中，训练数据在训练阶段可能会根据标记节点的可用性而变化——有标签、弱标签或没有标签。为了应对这些前所未有的挑战，该项目旨在开发新的方法。该研究将培养学生独立研究、进行科学实验、发表技术成果的能力，培养理工科研究人员。学生将接触到图形数据和机器学习的现实问题的核心技术。这项工作的影响还将扩展到对主流方法的批判性思维，理解诸如图少射节点分类等难题的本质，以及考虑现实场景探索简单有效的解决方案。情景元学习是目前的主流方法，已被证明是有效的监督少射节点分类。本项目对这种元学习方法的必要性提出了质疑，并阐述了需要一种新的图对比学习方法来进行少镜头节点分类，以处理有监督的少镜头节点分类，以及在训练过程中只有弱或没有监督信息的更具挑战性和现实的情况。本项目研究了另一种方法——图对比学习，为具有挑战性的少射节点分类问题寻找通用学习框架，并通过检查基础研究问题和开发监督、弱监督和自监督少射节点分类的新算法，处理训练过程中有噪声或无标签的情况。回顾了相关工作，介绍了各研究任务的初步研究，并提出了创新的研究任务，以制定原创和系统的解决方案。凭借PI团队在图学习方面的良好记录和对每个提出的研究任务的初步研究中获得的见解，该项目预计将为少量节点分类的图对比学习奠定坚实的基础，并为在训练过程中学习有噪声或无标签的图数据铺平道路。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。

A graph is a data structure consisting of nodes and edges. Graph data is the data associated with nodes and edges in a graph. Graph data is huge and is widely present in many real-world applications. Social media data is a typical example of graph data in which users are nodes and their relationships are edges. Since users have different profiles, they can form disparate relationships (i.e., edges) amongst themselves. When a dataset is large, annotating or labeling it with ground truth is time consuming and labor intensive. A pressing need for machine learning and data mining to effectively deal with big data like graph data is to address the labeled data scarcity problem. When we can only label a miniscule amount of data, can we learn well from big graph data? Graph few-shot node classification, in which learning can occur when only a small amount of data are labeled, is one such problem for which researchers strive to find novel solutions. In such a problem, training data can vary in the training phase depending on the availability of labeled nodes - with labels, weak labels, or no labels. To address such unprecedented challenges, this project aims to develop new approaches. The proposed research will train students to perform independent research, conduct scientific experiments, and publish technical results to nurture science and engineering researchers. Students will be exposed to the core techniques of real-world problems with graph data and machine learning. The impact of this work will also extend to critical thinking of dominant approaches, understanding the essence of difficult problems such as graph few-shot node classification, and exploring simple and effective solutions considering real-world scenarios.Episodic meta-learning is currently the dominant approach that has been shown to be effective for supervised few-shot node classification. This project questions the necessity of this meta-learning approach and elaborates the need for a novel graph contrastive learning approach to few-shot node classification to handle supervised few-shot node classification and more challenging and realistic cases where only weak or no supervision information is available during training. This project investigates an alternative approach - graph contrastive learning in search of a general learning framework for the challenging problem of few-shot node classification and to handle the cases with noisy or no labels during training by examining fundamental research issues and developing new algorithms for supervised, weakly supervised, and self-supervised few-shot node classification. Related work is reviewed, preliminary studies related to each research task are presented, and innovative research tasks are proposed to develop original and systematic solutions. With the proven track record in graph learning and insights gained in the preliminary studies for each proposed research task by the PI’s team, this project is envisioned as laying a solid foundation for graph contrastive learning for few-shot node classification and paving the way to advance the frontier of learning graph data with noisy or no labels during training.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.