Dynamical processes in complex network based on machine learning

• Grant number: 16/23698-1
• Support type: Scholarships in Brazil - Post-Doctorate
• Effective date (Start): July 01, 2017
• Status: Discontinued
• Field of knowledge: Physical Sciences and Mathematics - Computer Science
• Principal Investigator: Zhao Liang
• Grantee: Didier Augusto Vega Oliveros
• Home Institution: Faculdade de Filosofia, Ciências e Letras de Ribeirão Preto (FFCLRP). Universidade de São Paulo (USP). Ribeirão Preto , SP, Brazil
• Associated research grant: 15/50122-0 - Dynamic phenomena in complex networks: basics and applications, AP.TEM
• Associated scholarship(s): 18/24260-5 - Spatiotemporal data analytics based on complex networks, BE.EP.PD

Abstract

Propagation processes are ubiquitous in many complex network-based systems. The spread of epidemics, labels or information share similar characteristics and depend profoundly on the organization of the network. The complex networks have heterogeneous nature, where some vertices are more influential than others and there are different types of vertices connected to each other. In this way, understanding how the network structure impacts the dynamics and also how to infer the structure from these dynamics is of paramount importance to the area. In this project, we aim to develop methods to improve the tasks of the dynamical processes of machine learning in complex networks, analyzing the impact that the network exerts on them. We will analyze which vertices are most influential in the label propagation task and which can be recommended to be labeled in order to maximize the accuracy results. Also, we will develop methods to detect the community structure of the network from the propagation dynamics. Finally, through the use of multilayer networks, we will develop a method of selection of attributes proper to networks. The analyses will be conducted considering the theory of complex networks and machine learning, using artificial and real databases, evaluating the methods of the literature and applying to possible real problems.

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)

ANGHINONI, LEANDRO; ZHAO, LIANG; JI, DONGHONG; PAN, HENG. Time series trend detection and forecasting using complex network topology analysis. NEURAL NETWORKS, v. 117, p. 295-306, SEP 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.

VEGA-OLIVEROS, DIDIER A.; MENDEZ-BERMULEZ, J. A.; RODRIGUES, FRANCISCO A. Multifractality in random networks with power-law decaying bond strengths. Physical Review E, v. 99, n. 4 APR 10 2019. Web of Science Citations: 0.