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

III：SMALL：少样本节点分类的图对比学习

• 批准号: 2229461
• 负责人: Huan Liu
• 金额: $ 60万
• 依托单位: Arizona State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-01-01 至 2025-12-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2229461&HistoricalAwards=false
• 关键词: III; SMALL; Graph; Contrastive; Learning

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.

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，并且广泛存在于许多现实世界的应用中。由于用户具有不同的配置文件，因此他们之间可以形成不同的关系（即边缘），当数据集很大时，用基本事实对其进行注释或标记是耗时且费力的。 .对机器的迫切需求有效处理图数据等大数据的学习和数据挖掘是为了解决标记数据稀缺问题，当我们只能标记极少量的数据时，我们可以从大图数据中很好地学习吗？当只有少量数据被标记时，学习就可以发生，这是研究人员努力寻找新解决方案的一个这样的问题。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（带有标签）的可用性而变化。 、弱标签或无标签。该项目旨在开发新方法，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接触现实世界问题的核心技术。这项工作的影响还将扩展到对主流方法的批判性思维，理解图形小样本节点分类等难题的本质，并探索考虑现实世界场景的简单有效的解决方案。 - 学习是目前的主要方法，已被证明是有效的该项目质疑这种元学习方法的必要性，并阐述了对少样本节点分类的新型图对比学习方法的需求，以处理有监督的少样本节点分类以及更具挑战性和现实的情况，其中在训练期间仅提供弱监督信息或没有监督信息可用，这提出了一种替代方法 - 图对比学习，以寻找通用学习框架来解决少样本节点调查分类的挑战性问题，并处理训练期间有噪声或无标签的情况。通过检查基础研究问题并开发新算法回顾了相关工作，介绍了与每个研究任务相关的初步研究，并提出了创新的研究任务，以开发原始且系统的解决方案，并在图中得到了可靠的记录。结合 PI 团队在每个提出的研究任务的初​​步研究中获得的学习和见解，该项目预计将为少样本节点分类的图对比学习奠定坚实的基础，并为推进学习图数据的前沿铺平道路。期间有噪音或没有标签该奖项反映了 NSF 的法定使命，并通过使用基金会的智力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

Supervised Graph Contrastive Learning for Few-Shot Node Classification

用于少样本节点分类的监督图对比学习

• DOI: 10.1007/978-3-031-26390-3_24
• 发表时间: 2022-03-29
• 作者: Zhen Tan;Kaize Ding;Ruocheng Guo;Huan Liu
• 通讯作者: Huan Liu

Virtual Node Tuning for Few-shot Node Classification

针对少样本节点分类的虚拟节点调整

• DOI: 10.1145/3580305.3599541
• 发表时间: 2023-06-09
• 期刊: Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
• 作者: Zhen Tan;Ruocheng Guo;Kaize Ding;Huan Liu
• 通讯作者: Huan Liu

Inductive Linear Probing for Few-Shot Node Classification

用于少样本节点分类的感应线性探测

• 发表时间: 2023-09
• 期刊: Springer
• 作者: Mathavan, Hirthik;Tan, Zhen;Mudiam, Nivedh;Liu, Huan
• 通讯作者: Liu, Huan

Contrastive Meta-Learning for Few-shot Node Classification

用于少样本节点分类的对比元学习

• DOI: 10.1145/3580305.3599288
• 发表时间: 2023-08
• 期刊: ACM
• 作者: Wang, Song;Tan, Zhen;Liu, Huan;Li, Jundong
• 通讯作者: Li, Jundong