Knowledge acquisition: A Complex networks approach

Autor(es)

de Arruda, Henrique F. ^[1] ; Silva, Filipi N. ^[2] ; Costa, Luciano da F. ^[2] ; Amancio, Diego R. ^[1]

Afiliação do(s) autor(es)

^[1] Univ Sao Paulo, Inst Math & Comp Sci, POB 668, BR-13560970 Sao Carlos, SP - Brazil

^[2] Univ Sao Paulo, Sao Carlos Inst Phys, POB 369, BR-13560970 Sao Carlos, SP - Brazil

• Tipo de documento: Artigo Científico
• Fonte: INFORMATION SCIENCES; v. 421, p. 154-166, DEC 2017.
• Citações Web of Science: 12

Resumo

Complex networks have been found to provide a good representation of knowledge. In this context, the discovery process can be modeled in terms of a dynamic process such as agents moving in a knowledge space. Recent studies proposed more realistic dynamics which can be influenced by the visibility of the agents, or by their memory. However, rather than dealing with these two concepts separately, in this study we propose a multi-agent random walk model for knowledge acquisition that integrates both these aspects. More specifically, we employed the true self avoiding walk modified to incorporate a type of stochastic flight. Such flights depend on fields of visibility emanating from the various agents, in an attempt to model the influence between researchers. The proposed framework has been illustrated considering a set of network models and two real-world networks, one generated from Wikipedia (articles from biology and mathematics) and another from the Web of Science comprising only the area of complex networks. The results were analyzed globally and by regions. In the global analysis, we found that most of the dynamics parameters do not affect significantly the discovery process. Yet, the local analysis revealed a substantial difference in performance, depending on the local topology. In particular, dynamics taking place at the core of the networks tended to enhance knowledge acquisition. The choice of the parameters controlling the dynamics were found to have little impact on the performance for the considered knowledge networks, even at the local scale. (C) 2017 Elsevier Inc. All rights reserved. (AU)

• Processo FAPESP: 16/19069-9 - Classificação de documentos usando informações semânticas em redes complexas
• Beneficiário: Diego Raphael Amancio
• Linha de fomento: Auxílio à Pesquisa - Regular
• Processo FAPESP: 14/20830-0 - Modelagem e reconhecimento de padrões em textos com redes complexas
• Beneficiário: Diego Raphael Amancio
• Linha de fomento: Auxílio à Pesquisa - Regular
• Processo FAPESP: 15/08003-4 - Abordagem de redes complexas em e-Science e dados dinâmicos
• Beneficiário: Filipi Nascimento Silva
• Linha de fomento: Bolsas no Brasil - Pós-Doutorado
• Processo FAPESP: 11/50761-2 - Modelos e métodos de e-Science para ciências da vida e agrárias
• Beneficiário: Roberto Marcondes Cesar Junior
• Linha de fomento: Auxílio à Pesquisa - Temático