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

• Grant number: 18/01722-3
• Support Opportunities: Regular Research Grants
• Start date: October 01, 2018
• End date: November 30, 2020
• Field of knowledge: Physical Sciences and Mathematics - Computer Science - Computer Systems
• Principal Investigator: Lilian Berton
• Grantee: Lilian Berton
• Host Institution: Instituto de Ciência e Tecnologia (ICT). Universidade Federal de São Paulo (UNIFESP). Campus São José dos Campos. São José dos Campos , SP, Brazil
• City of the host institution: São José dos Campos
• Associated researchers: Didier Augusto Vega Oliveros ; Elbert Einstein Nehrer Macau ; Evangelos Milios ; Otávio Augusto Bizetto Penatti
• Associated scholarship(s): 19/24951-0 - Semi-supervised learning based on complex networks and applications: climate data analysis, BP.TT; 19/17292-0 - Semi-supervised learning based on complex networks and applications: text mining, BP.TT; 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

Abstract

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

(17)

(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. (18/01722-3, 18/24260-5, 16/23698-1, 15/50122-0)

VEGA-OLIVEROS, DIDIER A.; ZHAO, LIANG; BERTON, LILIAN. Evaluating link prediction by diffusion processes in dynamic networks. SCIENTIFIC REPORTS, v. 9, JUL 25 2019. (18/24260-5, 13/07375-0, 18/01722-3, 16/23698-1, 15/50122-0)

DE CASTRO SANTOS, MATHEUS A.; VEGA-OLIVEROS, DIDIER A.; ZHAO, LIANG; BERTON, LILIAN. Classifying El Ni & x00F1;o-Southern Oscillation Combining Network Science and Machine Learning. IEEE ACCESS, v. 8, p. 55711-55723, 2020. (18/01722-3, 18/24260-5, 16/23698-1, 15/50122-0, 18/04029-7)

VEGA-OLIVEROS, DIDIER A.; ZHAO, LIANG; ROCHA, ANDERSON; BERTON, LILIAN. Link Prediction Based on Stochastic Information Diffusion. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, FEB 2021. (13/07375-0, 18/24260-5, 19/07665-4, 16/23698-1, 15/50122-0, 17/12646-3, 18/01722-3, 19/26283-5)

DUARTE, JOSE MARCIO; SOUSA, SAMUEL; MILIOS, EVANGELOS; BERTON, LILIAN. Deep analysis of word sense disambiguation via semi-supervised learning and neural word representations. INFORMATION SCIENCES, v. 570, p. 278-297, SEP 2021. (18/01722-3, 18/09465-0)

COTACALLAPA, MOSHE; BERTON, LILIAN; FERREIRA, LEONARDO N.; QUILES, MARCOS G.; ZHAO, LIANG; MACAU, ELBERT E. N.; VEGA-OLIVEROS, DIDIER A.; IEEE. Measuring the engagement level in encrypted group conversations by using temporal networks. 2020 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN), v. N/A, p. 8-pg., 2020-01-01. (17/05831-9, 16/23698-1, 16/16291-2, 18/24260-5, 15/50122-0, 18/01722-3, 19/00157-3)

DE OLIVEIRA, WILLIAN DIHANSTER G.; PENATTI, OTAVIO A. B.; BERTON, LILIAN; IEEE. A comparison of graph-based semi-supervised learning for data augmentation. 2020 33RD SIBGRAPI CONFERENCE ON GRAPHICS, PATTERNS AND IMAGES (SIBGRAPI 2020), v. N/A, p. 8-pg., 2020-01-01. (18/01722-3)

VEGA-OLIVEROS, DIDIER A.; COTACALLAPA, MOSHE; FERREIRA, LEONARDO N.; QUILES, MARCOS G.; ZHAO, LIANG; MACAU, ELBERT E. N.; CARDOSO, MANOEL F.; ASSOC COMP MACHINERY. From spatio-temporal data to chronological networks: An application to wildfire analysis. SAC '19: PROCEEDINGS OF THE 34TH ACM/SIGAPP SYMPOSIUM ON APPLIED COMPUTING, v. N/A, p. 8-pg., 2019-01-01. (18/24260-5, 18/01722-3, 15/50122-0, 16/23698-1, 11/18496-7, 17/05831-9, 16/16291-2)

FREITAS, VANDER L. S.; YANCHUK, SERHIY; ZAKS, MICHAEL; MACAU, ELBERT E. N.. Synchronization-based symmetric circular formations of mobile agents and the generation of chaotic trajectories. COMMUNICATIONS IN NONLINEAR SCIENCE AND NUMERICAL SIMULATION, v. 94, MAR 2021. (17/24224-6, 17/04552-9, 15/50122-0, 18/01722-3)

BERTON, LILIAN; DE OLIVEIRA, PEDRO LEYRIA; VEGA-OLIVEROS, DIDIER A.. Graph-based semi-supervised classification for similar wildfire dynamics. 38TH ANNUAL ACM SYMPOSIUM ON APPLIED COMPUTING, SAC 2023, v. N/A, p. 8-pg., 2023-01-01. (19/26283-5, 18/01722-3, 22/16655-5)

FERREIRA, LEONARDO N.; VEGA-OLIVEROS, DIDIER A.; ZHAO, LIANG; CARDOSO, MANOEL F.; MACAU, ELBERT E. N.. Global fire season severity analysis and forecasting. Computers & Geosciences, v. 134, JAN 2020. (13/07375-0, 17/05831-9, 16/23698-1, 15/50122-0, 19/00157-3, 18/03211-6, 18/01722-3)

SANO, HUMBERTO HAYASHI; BERTON, LILIAN; ACM. Topology and Robustness Analysis of Temporal Air Transport Network. PROCEEDINGS OF THE 35TH ANNUAL ACM SYMPOSIUM ON APPLIED COMPUTING (SAC'20), v. N/A, p. 4-pg., 2020-01-01. (18/01722-3)

DE AQUINO AFONSO, BRUNO KLAUS; BERTON, LILIAN; ACM. Analysis of label noise in graph-based semi-supervised learning. PROCEEDINGS OF THE 35TH ANNUAL ACM SYMPOSIUM ON APPLIED COMPUTING (SAC'20), v. N/A, p. 8-pg., 2020-01-01. (18/15014-0, 18/01722-3)

DE AQUINO AFONSO, BRUNO KLAUS; BERTON, LILIAN. Identifying noisy labels with a transductive semi-supervised leave-one-out filter. PATTERN RECOGNITION LETTERS, v. 140, p. 127-134, DEC 2020. (18/01722-3, 18/15014-0)

SOUSA, SAMUEL; MILIOS, EVANGELOS; BERTON, LILIAN; IEEE. Word sense disambiguation: an evaluation study of semi-supervised approaches with word embeddings. 2020 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN), v. N/A, p. 8-pg., 2020-01-01. (18/01722-3, 18/09465-0)

SANO, HUMBERTO HAYASHI; BERTON, LILIAN. Temporal Event Graph Approach and Robustness Analysis for Air Transport Networ. IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING, v. 8, n. 4, p. 3453-3464, OCT 1 2021. (18/01722-3)

BERTON, LILIAN; MITSUISHI, FELIPE BAZ; VEGA-OLIVEROS, DIDIER A.. Analysis of active semi-supervised learning. 38TH ANNUAL ACM SYMPOSIUM ON APPLIED COMPUTING, SAC 2023, v. N/A, p. 8-pg., 2023-01-01. (20/09932-7, 22/16655-5, 19/26283-5, 18/01722-3)