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Semi-supervised learning via complex networks: network construction, selection and propagation of labels and applications

Grant number: 18/01722-3
Support type:Regular Research Grants
Duration: October 01, 2018 - September 30, 2020
Field of knowledge:Physical Sciences and Mathematics - Computer Science
Principal Investigator:Lilian Berton
Grantee:Lilian Berton
Home Institution: Instituto de Ciência e Tecnologia (ICT). Universidade Federal de São Paulo (UNIFESP). Campus São José dos Campos. São José dos Campos , SP, Brazil
Assoc. researchers:Didier Augusto Vega Oliveros ; Elbert Einstein Nehrer Macau ; Evangelos Milios ; Otávio Augusto Bizetto Penatti
Associated scholarship(s):18/22258-3 - Semi-supervised learning based on complex networks and applications: climate data analysis, BP.TT
18/22264-3 - Semi-supervised learning based on complex networks and applications: text mining, BP.TT


Graph-based semi-supervised learning (SSL) is a promising paradigm for modeling the manifold in multidimensional data space, and is effective in propagating a small number of initial labels to a large amount of unlabelled data. This approach has been used in a variety of applications, such as image segmentation and annotation, human speech recognition, text classification, etc. Recently, some authors observed the importance of the generated network for the label propagation process, but other aspects were still little investigated, for example, the selection of the initial labeled samples or the topological characteristics of the network. Thus, the objective of this project is to investigate in depth all the steps involved in SSL, including the selection of initial labels, the selection of the network to be built and the method for propagating labels, and propose new approaches to improve this process. Challenges of the area will also be addressed such as contaminated or unbalanced labels, large databases and applications in text mining, climate data analysis and data augmentation for images. (AU)

Scientific publications
(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)
VEGA-OLIVERO, DIDIER A.; GOMES, PEDRO SPOLJARIC; MILIOS, EVANGELOS E.; BERTON, LILIAN. A multi-centrality index for graph-based keyword extraction. INFORMATION PROCESSING & MANAGEMENT, v. 56, n. 6 NOV 2019. Web of Science Citations: 0.
VEGA-OLIVEROS, DIDIER A.; ZHAO, LIANG; BERTON, LILIAN. Evaluating link prediction by diffusion processes in dynamic networks. SCIENTIFIC REPORTS, v. 9, JUL 25 2019. Web of Science Citations: 0.

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