Research Topic

Science of Science: a Complex Network Perspective

About this Research Topic

The study of the structure of science and how it evolves has been leading to significant breakthroughs in many research fields. The use of statistical and quantitative methods sheds light on assessing and comparing the quality, quantity, diversity, and other characteristics among research entities (such as papers, journals, and authors). This approach provided better ways for stakeholders to develop and implement policies to improve the scientific environment. Also, the new methodologies led by the recent advancements in network science and neural networks when applied to scholarly datasets have been displaying potential to accelerate discoveries and ease education and dissemination of science. For instance, understanding the growth and how the topics evolve in a given research field is a fundamental task to develop high-quality literature reviews, which can be used to make better science or for education purposes.

Many complementary aspects of science have been studied, including the analysis of citation networks, co-authorship patterns, textual content, topics, among others. Due to the nature of this type of data, network science has become vital to the development of the science of science field. Furthermore, information about scientific studies and the relationship between authors are becoming more substantial due to the availability of massive data, which gives rise to studies incorporating sophisticated data science and machine learning techniques.

In this Research Topic, we invite authors to send their contributions to approaches and analyses using complex networks in the field of science of science. The topics include, but are not restricted to:

- Models of dynamics (e.g., scientific evolution, citations, collaboration, growth of areas, among others);
- Multi-agent discovery;
- Mechanistic models for citation dynamics;
- Analysis of textual features of scientific documents;
- Analysis of career trajectories of scholars;
- Birth, growth, and death of fields of study;
- Comparison between distinct fields of study;
- Temporal series analysis obtained from scientific entities;
- Representation and analysis of science by using time-varying networks;
- Analysis and development of network science tools;
- Multilayer, multiplex representations of knowledge;
- Analysis of high order structures (e.g., hypergraphs and simplex);
- Academic productivity and bibliometric indices;
- Analysis via visualization techniques;
- Case studies of specific science of science questions;
- Querying, storing, and handling large scholarly datasets;
- Predicting the success of scientific entities;
- Embedding scientific entities.


Keywords: science of science, scientific careers, citation network, bibliographic database, author citations, hot streak, citation history, network science, data mining, collaboration networks, bibliometrics, academic productivity, machine learning, neural networks


Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

The study of the structure of science and how it evolves has been leading to significant breakthroughs in many research fields. The use of statistical and quantitative methods sheds light on assessing and comparing the quality, quantity, diversity, and other characteristics among research entities (such as papers, journals, and authors). This approach provided better ways for stakeholders to develop and implement policies to improve the scientific environment. Also, the new methodologies led by the recent advancements in network science and neural networks when applied to scholarly datasets have been displaying potential to accelerate discoveries and ease education and dissemination of science. For instance, understanding the growth and how the topics evolve in a given research field is a fundamental task to develop high-quality literature reviews, which can be used to make better science or for education purposes.

Many complementary aspects of science have been studied, including the analysis of citation networks, co-authorship patterns, textual content, topics, among others. Due to the nature of this type of data, network science has become vital to the development of the science of science field. Furthermore, information about scientific studies and the relationship between authors are becoming more substantial due to the availability of massive data, which gives rise to studies incorporating sophisticated data science and machine learning techniques.

In this Research Topic, we invite authors to send their contributions to approaches and analyses using complex networks in the field of science of science. The topics include, but are not restricted to:

- Models of dynamics (e.g., scientific evolution, citations, collaboration, growth of areas, among others);
- Multi-agent discovery;
- Mechanistic models for citation dynamics;
- Analysis of textual features of scientific documents;
- Analysis of career trajectories of scholars;
- Birth, growth, and death of fields of study;
- Comparison between distinct fields of study;
- Temporal series analysis obtained from scientific entities;
- Representation and analysis of science by using time-varying networks;
- Analysis and development of network science tools;
- Multilayer, multiplex representations of knowledge;
- Analysis of high order structures (e.g., hypergraphs and simplex);
- Academic productivity and bibliometric indices;
- Analysis via visualization techniques;
- Case studies of specific science of science questions;
- Querying, storing, and handling large scholarly datasets;
- Predicting the success of scientific entities;
- Embedding scientific entities.


Keywords: science of science, scientific careers, citation network, bibliographic database, author citations, hot streak, citation history, network science, data mining, collaboration networks, bibliometrics, academic productivity, machine learning, neural networks


Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

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Submission Deadlines

17 October 2021 Abstract
15 January 2022 Manuscript

Participating Journals

Manuscripts can be submitted to this Research Topic via the following journals:

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Topic Editors

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Submission Deadlines

17 October 2021 Abstract
15 January 2022 Manuscript

Participating Journals

Manuscripts can be submitted to this Research Topic via the following journals:

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