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

TL;DR

This paper presents a combined network science and text analytics approach to automatically map and analyze the structure, subfields, and evolution of scientific disciplines using large-scale citation and text data.

Contribution

It introduces a novel methodology integrating network analysis and text analytics to construct scientific taxonomies and visualize the relatedness and evolution of research topics.

Findings

Identified distinct communities within photonic crystals aligning with researcher groups.

Mapped interdisciplinary and temporal dynamics of subtopics through keywords.

Demonstrated the methodology on complex networks and photonic crystals datasets.

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.