Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Text Classification from Labeled and Unlabeled Documents using EM
Machine Learning - Special issue on information retrieval
Machine Learning
Handbook of Graphs and Networks: From the Genome to the Internet
Handbook of Graphs and Networks: From the Genome to the Internet
Learning from Labeled and Unlabeled Data using Graph Mincuts
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Laplacian Eigenmaps for dimensionality reduction and data representation
Neural Computation
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Tri-Training: Exploiting Unlabeled Data Using Three Classifiers
IEEE Transactions on Knowledge and Data Engineering
Beyond the point cloud: from transductive to semi-supervised learning
ICML '05 Proceedings of the 22nd international conference on Machine learning
Semi-supervised learning with graphs
Semi-supervised learning with graphs
Unsupervised Learning of Image Manifolds by Semidefinite Programming
International Journal of Computer Vision
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Random Walks for Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Semi-Supervised Learning (Adaptive Computation and Machine Learning)
Semi-Supervised Learning (Adaptive Computation and Machine Learning)
Label Propagation through Linear Neighborhoods
IEEE Transactions on Knowledge and Data Engineering
A hybrid generative/discriminative approach to semi-supervised classifier design
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
Semi-supervised classification using local and global regularization
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
Preventing error propagation in semi-supervised learning
ISNN'12 Proceedings of the 9th international conference on Advances in Neural Networks - Volume Part I
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Semi-supervised learning techniques have gained increasing attention in the machine learning community, as a result of two main factors: (1) the available data is exponentially increasing; (2) the task of data labeling is cumbersome and expensive, involving human experts in the process. In this paper, we propose a network-based semi-supervised learning method inspired by the modularity greedy algorithm, which was originally applied for unsupervised learning. Changes have been made in the process of modularity maximization in a way to adapt the model to propagate labels throughout the network. Furthermore, a network reduction technique is introduced, as well as an extensive analysis of its impact on the network. Computer simulations are performed for artificial and real-world databases, providing a numerical quantitative basis for the performance of the proposed method.