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

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