Graph-Based Semi-Supervised Learning: A Comprehensive Review

Semi-supervised learning (SSL) has tremendous value in practice due to the utilization of both labeled and unlabelled data. An essential class of SSL methods, referred to as graph-based semi-supervised learning (GSSL) methods in the literature, is to first represent each sample as a node in an affin...

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Bibliographic Details
Published in:IEEE transaction on neural networks and learning systems Vol. 34; no. 11; pp. 8174 - 8194
Main Authors: Song, Zixing, Yang, Xiangli, Xu, Zenglin, King, Irwin
Format: Journal Article
Language:English
Published: United States IEEE 01.11.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Codes
Graph embedding
graph representation learning
graph-based semi-supervised learning (GSSL)
Image color analysis
Learning
Manifolds
Prediction algorithms
Semi-supervised learning
semi-supervised learning (SSL)
Semisupervised learning
Taxonomy
Training
ISSN:2162-237X, 2162-2388, 2162-2388
Online Access:Get full text
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Summary:Semi-supervised learning (SSL) has tremendous value in practice due to the utilization of both labeled and unlabelled data. An essential class of SSL methods, referred to as graph-based semi-supervised learning (GSSL) methods in the literature, is to first represent each sample as a node in an affinity graph, and then, the label information of unlabeled samples can be inferred based on the structure of the constructed graph. GSSL methods have demonstrated their advantages in various domains due to their uniqueness of structure, the universality of applications, and their scalability to large-scale data. Focusing on GSSL methods only, this work aims to provide both researchers and practitioners with a solid and systematic understanding of relevant advances as well as the underlying connections among them. The concentration on one class of SSL makes this article distinct from recent surveys that cover a more general and broader picture of SSL methods yet often neglect the fundamental understanding of GSSL methods. In particular, a significant contribution of this article lies in a newly generalized taxonomy for GSSL under the unified framework, with the most up-to-date references and valuable resources such as codes, datasets, and applications. Furthermore, we present several potential research directions as future work with our insights into this rapidly growing field.
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ISSN:2162-237X
2162-2388
2162-2388
DOI:10.1109/TNNLS.2022.3155478