Improved Graph-based semi-supervised learning Schemes
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Abstract (arXiv)
In this work, we improve the accuracy of several known algorithms to address the classification of large datasets when few labels are available. Our framework lies in the realm of graph-based semi-supervised learning. With novel modifications on Gaussian Random Fields Learning and Poisson Learning algorithms, we increase the accuracy and create more robust algorithms. Experimental results demonstrate the efficiency and superiority of the proposed methods over conventional graph-based semi-supervised techniques, especially in the context of imbalanced datasets.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 13 of its 35 references that are in the map. First posted on arXiv 30 Jun 2024.
Builds on
13 of 35 references.
References · 13
- 2017Graph Attention Networkscited by 8,854
- 2016node2vec: Scalable Feature Learning for Networkscited by 1,913
- 2014DeepWalk: Online Learning of Social Representationscited by 1,543
- 2021Graph-based Semi-supervised Learning: A Comprehensive Reviewcited by 79
- 2020Poisson Learning: Graph Based Semi-Supervised Learning At Very Low Label Ratescited by 49
- 2017Analysis of p-Laplacian Regularization in Semi-Supervised Learningcited by 39
- 2017The game theoretic p-Laplacian and semi-supervised learning with few labelscited by 22
- 2018Properly-weighted graph Laplacian for semi-supervised learningcited by 19
- 2015Algorithms for Lipschitz Learning on Graphscited by 16
- 2017Consistency of Lipschitz learning with infinite unlabeled data and finite labeled datacited by 15
- 2023Semi-Supervised Graph Imbalanced Regressioncited by 14
- 2023From Cluster Assumption to Graph Convolution: Graph-based Semi-Supervised Learning Revisitedcited by 6
1 more
- 2023Graph Laplacian for Semi-Supervised Learningcited by 5
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Citing papers per year: 2025: 2.
2 papers.