Graph-based Semi-supervised Learning: A Comprehensive Review

by   Zixing Song, et al.

Semi-supervised learning (SSL) has tremendous value in practice due to its ability to utilize both labeled data and unlabelled data. An important class of SSL methods is to naturally represent data as graphs such that the label information of unlabelled samples can be inferred from the graphs, which corresponds to graph-based semi-supervised learning (GSSL) methods. 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 this class of methods, 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. This makes our paper distinct from recent surveys that cover an overall picture of SSL methods while neglecting fundamental understanding of GSSL methods. In particular, a major contribution of this paper lies in a new generalized taxonomy for GSSL, including graph regularization and graph embedding methods, with the most up-to-date references and useful resources such as codes, datasets, and applications. Furthermore, we present several potential research directions as future work with insights into this rapidly growing field.



There are no comments yet.


page 1


A Survey on Deep Semi-supervised Learning

Deep semi-supervised learning is a fast-growing field with a range of pr...

A Flexible Generative Framework for Graph-based Semi-supervised Learning

We consider a family of problems that are concerned about making predict...

Machine Learning on Graphs: A Model and Comprehensive Taxonomy

There has been a surge of recent interest in learning representations fo...

On the Supermodularity of Active Graph-based Semi-supervised Learning with Stieltjes Matrix Regularization

Active graph-based semi-supervised learning (AG-SSL) aims to select a sm...

Almost exact recovery in noisy semi-supervised learning

This paper investigates noisy graph-based semi-supervised learning or co...

Are Negative Samples Necessary in Entity Alignment? An Approach with High Performance, Scalability and Robustness

Entity alignment (EA) aims to find the equivalent entities in different ...

Generalized Matrix Means for Semi-Supervised Learning with Multilayer Graphs

We study the task of semi-supervised learning on multilayer graphs by ta...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.