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。图形数据是巨大的，广泛存在于许多现实世界的应用程序。社交媒体数据是图数据的典型示例，其中用户是节点，并且他们的关系是边。由于用户具有不同的简档，因此他们可以形成完全不同的关系（即，边）之间。当数据集很大时，使用地面实况对其进行注释或标记是耗时且劳动密集型的。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是解决标记数据稀缺问题。当我们只能标记极少量的数据时，我们能从大图数据中很好地学习吗？图少镜头节点分类，其中学习可以发生在只有少量的数据被标记时，是研究人员努力寻找新的解决方案的一个这样的问题。 在这样的问题中，训练数据可以在训练阶段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。拟议的研究将培养学生进行独立研究，进行科学实验，并发表技术成果，以培养科学和工程研究人员。学生将接触到图形数据和机器学习的真实世界问题的核心技术。这项工作的影响也将扩展到主流方法的批判性思维，理解困难问题的本质，如图少镜头节点分类，并探索简单有效的解决方案，考虑到现实世界的场景。情节元学习是目前的主流方法，已被证明是有效的监督少镜头节点分类。该项目质疑这种元学习方法的必要性，并阐述了需要一种新的图对比学习方法来处理监督的少数节点分类，以及在训练过程中只有弱信息或没有监督信息的更具挑战性和现实性的情况。该项目研究了另一种方法-图对比学习，以寻找一个通用的学习框架来解决少数节点分类的挑战性问题，并通过研究基础研究问题和开发新的监督，弱监督和自监督少数节点分类算法来处理训练过程中有噪声或无标签的情况。相关的工作进行了审查，初步研究相关的每个研究任务，并提出了创新的研究任务，以开发原始的和系统的解决方案。凭借在图形学习方面的良好记录以及PI团队在每个拟议研究任务的初步研究中获得的见解，该项目被设想为为少数人的图形对比学习奠定坚实的基础，镜头节点分类，并为推进训练过程中带有噪声或没有标签的学习图数据的前沿铺平道路。该奖项反映了NSF的法定使命，并被认为值得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来提供支持。

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.