Social network analysis and mining

• Grant number: 15/14228-9
• Support type: Regular Research Grants
• Duration: December 01, 2015 - November 30, 2018
• Field of knowledge: Physical Sciences and Mathematics - Computer Science
• Principal Investigator: Alneu de Andrade Lopes
• Grantee: Alneu de Andrade Lopes
• Home Institution: Instituto de Ciências Matemáticas e de Computação (ICMC). Universidade de São Paulo (USP). São Carlos , SP, Brazil
• Assoc. researchers: Alan Demetrius Baria Valejo ; Jorge Carlos Valverde Rebaza ; Ricardo Miguel Puma Alvarez
• Associated grant(s): 17/50153-9 - Graph-based total recall information retrieval on text document corpora, AP.R

Abstract

Online social networks are web platforms that reflect the social networking structures of the real world, such as friendship, professional, family, and other networks. In recent years, the study of social networks has attracted the attention of the scientific community for its variety of applications such as: friendship, locations, and products recommendation systems, travel planning systems, and social cataloging, which allows users organize their collections (books, music, places visited, among others) as they interact with others and write down their impressions and recommendations. Some of these networks contains geolocation information of users which allow a wider range of applications. Given the dynamic and temporal nature, heterogeneous and eventually georeferenced social networks, there are different problems to be faced. This project addresses the following issues 1) formation of new relationships between users; 2) detection of communities and 3) behavioral analysis of group of users. The project relates to two doctoral projects and one MSc research, all in progress. It is observed that most of the research in social networks addresses only the analysis of the information related to the pair of user or their neighborhood, that is the local information. We expect this project to investigate in depth how the behavior of groups of users, and additional data such as geolocation and time in heterogeneous networks impact the problem of creating new relationships and dynamics of social networks. (AU)

Scientific publications

(7)

(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)

VALEJO, ALAN; FERREIRA DE OLIVEIRA, MARIA CRISTINA; FILHO, GERALDO P. R.; LOPES, ALNEU DE ANDRADE. Multilevel approach for combinatorial optimization in bipartite network. KNOWLEDGE-BASED SYSTEMS, v. 151, p. 45-61, JUL 1 2018. Web of Science Citations: 1.

VALVERDE-REBAZA, JORGE C.; ROCHE, MATHIEU; PONCELET, PASCAL; LOPES, ALNEU DE ANDRADE. The role of location and social strength for friendship prediction in location-based social networks. INFORMATION PROCESSING & MANAGEMENT, v. 54, n. 4, p. 475-489, JUL 2018. Web of Science Citations: 2.

CORREA, JR., EDILSON A.; LOPES, ALNEU A.; AMANCIO, DIEGO R. Word sense disambiguation: A complex network approach. INFORMATION SCIENCES, v. 442, p. 103-113, MAY 2018. Web of Science Citations: 5.

DRURY, BRETT; VALVERDE-REBAZA, JORGE; MOURA, MARIA-FERNANDA; LOPES, ALNEU DE ANDRADE. A survey of the applications of Bayesian networks in agriculture. ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, v. 65, p. 29-42, OCT 2017. Web of Science Citations: 10.

ROSSI, RAFAEL GERALDELI; LOPES, ALNEU DE ANDRADE; REZENDE, SOLANGE OLIVEIRA. Using bipartite heterogeneous networks to speed up inductive semi-supervised learning and improve automatic text categorization. KNOWLEDGE-BASED SYSTEMS, v. 132, p. 94-118, SEP 15 2017. Web of Science Citations: 1.

BERTON, LILIAN; FALEIROS, THIAGO DE PAULO; VALEJO, ALAN; VALVERDE-REBAZA, JORGE; LOPES, ALNEU DE ANDRADE. RGCLI: Robust Graph that Considers Labeled Instances for Semi Supervised Learning. Neurocomputing, v. 226, p. 238-248, FEB 22 2017. Web of Science Citations: 3.

FALEIROS, THIAGO DE PAULO; ROSSI, RAFAEL GERALDELI; LOPES, ALNEU DE ANDRADE. Optimizing the class information divergence for transductive classification of texts using propagation in bipartite graphs. PATTERN RECOGNITION LETTERS, v. 87, n. SI, p. 127-138, FEB 1 2017. Web of Science Citations: 0.