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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Using network science and text analytics to produce surveys in a scientific topic

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Author(s):
Silva, Filipi N. [1] ; Amancio, Diego R. [2] ; Bardosova, Maria [3] ; Costa, Luciano da F. [1] ; Oliveira, Jr., Osvaldo N. [1]
Total Authors: 5
Affiliation:
[1] Univ Sao Paulo, Sao Carlos Inst Phys, Sao Carlos, SP - Brazil
[2] Univ Sao Paulo, Inst Math & Comp Sci, Sao Carlos, SP - Brazil
[3] Tyndall Natl Inst, Cork - Ireland
Total Affiliations: 3
Document type: Journal article
Source: Journal of Informetrics; v. 10, n. 2, p. 487-502, MAY 2016.
Web of Science Citations: 25
Abstract

The use of science to understand its own structure is becoming popular, but understanding the organization of knowledge areas is still limited because some patterns are only discoverable with proper computational treatment of large-scale datasets. In this paper, we introduce a network-based methodology combined with text analytics to construct the taxonomy of science fields. The methodology is illustrated with application to two topics: complex networks (CN) and photonic crystals (PC). We built citation networks using data from the Web of Science and used a community detection algorithm for partitioning to obtain science maps of the fields considered. We also created an importance index for text analytics in order to obtain keywords that define the communities. A dendrogram of the relatedness among the subtopics was also obtained. Among the interesting patterns that emerged from the analysis, we highlight the identification of two well-defined communities in PC area, which is consistent with the known existence of two distinct communities of researchers in the area: telecommunication engineers and physicists. With the methodology, it was also possible to assess the interdisciplinary and time evolution of subtopics defined by the keywords. The automatic tools described here are potentially useful not only to provide an overview of scientific areas but also to assist scientists in performing systematic research on a specific topic. (C) 2016 Elsevier Ltd. All rights reserved. (AU)

FAPESP's process: 14/20830-0 - Using complex networks to recognize patterns in written texts
Grantee:Diego Raphael Amancio
Support type: Regular Research Grants
FAPESP's process: 11/50761-2 - Models and methods of e-Science for life and agricultural sciences
Grantee:Roberto Marcondes Cesar Junior
Support type: Research Projects - Thematic Grants
FAPESP's process: 15/08003-4 - Complex network approach to e-Science and dynamic datasets
Grantee:Filipi Nascimento Silva
Support type: Scholarships in Brazil - Post-Doctorate