Graph-based Semi-supervised Learning: A Comprehensive Review

• URL: http://arxiv.org/abs/2102.13303v1
• Date: Fri, 26 Feb 2021 05:11:09 GMT
• Title: Graph-based Semi-supervised Learning: A Comprehensive Review
• Authors: Zixing Song, Xiangli Yang, Zenglin Xu, Irwin King
• Abstract summary: 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, 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.
• Score: 51.26862262550445
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• Enhancing Graph Self-Supervised Learning with Graph Interplay [8.775644935074407]
  Graph Interplay (GIP) is an innovative and versatile approach that significantly enhances the performance equipped with various existing GSSL methods. GIP advocates introducing direct graph-level communications by random inter-graph edges within standard batches. Our empirical study demonstrates that GIP surpasses the performance of prevailing GSSL methods by significant margins.
  arXiv  Detail & Related papers  (2024-10-05T07:05:21Z)
• A Survey of the Self Supervised Learning Mechanisms for Vision Transformers [5.152455218955949]
  The application of self supervised learning (SSL) in vision tasks has gained significant attention. We develop a comprehensive taxonomy of systematically classifying the SSL techniques. We discuss the motivations behind SSL, review popular pre-training tasks, and highlight the challenges and advancements in this field.
  arXiv  Detail & Related papers  (2024-08-30T07:38:28Z)
• Language Semantic Graph Guided Data-Efficient Learning [10.039953846594805]
  We introduce a Language Semantic Graph (LSG) which is constructed from labels manifest as natural language descriptions. An auxiliary graph neural network is trained to extract high-level semantic relations and then used to guide the training of the primary model. Our in-depth analysis shows that the LSG method also expedites the training process.
  arXiv  Detail & Related papers  (2023-11-15T08:54:57Z)
• Self-Supervised Learning for Time Series Analysis: Taxonomy, Progress, and Prospects [84.6945070729684]
  Self-supervised learning (SSL) has recently achieved impressive performance on various time series tasks. This article reviews current state-of-the-art SSL methods for time series data.
  arXiv  Detail & Related papers  (2023-06-16T18:23:10Z)
• A Survey on Self-supervised Learning: Algorithms, Applications, and Future Trends [82.64268080902742]
  Self-supervised learning (SSL) aims to learn discriminative features from unlabeled data without relying on human-annotated labels. SSL has garnered significant attention recently, leading to the development of numerous related algorithms. This paper presents a review of diverse SSL methods, encompassing algorithmic aspects, application domains, three key trends, and open research questions.
  arXiv  Detail & Related papers  (2023-01-13T14:41:05Z)
• Analyzing Data-Centric Properties for Contrastive Learning on Graphs [32.69353929886551]
  We investigate how do graph SSL methods, such as contrastive learning (CL), work well? Our work rigorously contextualizes, both empirically and theoretically, the effects of data-centric properties on augmentation strategies and learning paradigms for graph SSL.
  arXiv  Detail & Related papers  (2022-08-04T17:58:37Z)
• Self-supervised on Graphs: Contrastive, Generative,or Predictive [25.679620842010422]
  Self-supervised learning (SSL) is emerging as a new paradigm for extracting informative knowledge through well-designed pretext tasks. We divide existing graph SSL methods into three categories: contrastive, generative, and predictive. We also summarize the commonly used datasets, evaluation metrics, downstream tasks, and open-source implementations of various algorithms.
  arXiv  Detail & Related papers  (2021-05-16T03:30:03Z)
• Self-Supervised Learning of Graph Neural Networks: A Unified Review [50.71341657322391]
  Self-supervised learning is emerging as a new paradigm for making use of large amounts of unlabeled samples. We provide a unified review of different ways of training graph neural networks (GNNs) using SSL. Our treatment of SSL methods for GNNs sheds light on the similarities and differences of various methods, setting the stage for developing new methods and algorithms.
  arXiv  Detail & Related papers  (2021-02-22T03:43:45Z)
• On Data-Augmentation and Consistency-Based Semi-Supervised Learning [77.57285768500225]
  Recently proposed consistency-based Semi-Supervised Learning (SSL) methods have advanced the state of the art in several SSL tasks. Despite these advances, the understanding of these methods is still relatively limited.
  arXiv  Detail & Related papers  (2021-01-18T10:12:31Z)
• Contrastive and Generative Graph Convolutional Networks for Graph-based Semi-Supervised Learning [64.98816284854067]
  Graph-based Semi-Supervised Learning (SSL) aims to transfer the labels of a handful of labeled data to the remaining massive unlabeled data via a graph. A novel GCN-based SSL algorithm is presented in this paper to enrich the supervision signals by utilizing both data similarities and graph structure.
  arXiv  Detail & Related papers  (2020-09-15T13:59:28Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.