Abstract

Scientific research is often thought of as being conducted by individuals and small teams striving for disciplinary advances. Yet as a whole, this endeavor more closely resembles a complex and integrated system of people, papers, and ideas. Studies of co-authorship and citation networks have revealed important structural properties of researchers and articles, but currently the structure of scientific ideas themselves is not well understood. In this study, we posit that topic networks may be a useful framework for revealing the nature of conceptual relationships. Using this framework, we map the landscape of interconnected research topics covered in the multidisciplinary journal PNAS since 2000, constructing networks in which nodes represent topics of study and edges give the extent to which topics occur in the same papers. The network displays small-world architecture, characterized by regions of dense local connectivity with sparse connectivity between them. In this network, dense local connectivity additionally gives rise to distinct clusters of related topics. Yet notably, these clusters tend not to align with assigned article classifications, and instead contain topics from various disciplines. Using a temporal graph, we find that small-worldness has increased over time, suggesting growing efficiency and integration of ideas. Finally, we define two measures of interdisciplinarity, one of which is found to be positively associated with PNAS’s impact factor. Broadly, this work suggests that complex and dynamic patterns of knowledge emerge from scientific research, and that structures reflecting intellectual integration may be beneficial for obtaining scientific insight.

Highlights

  • The practice of scientific research represents the collective effort of humans to acquire information, generate insight, and disseminate knowledge

  • As further confirmation of the data-driven partition’s characterization of the community structure, we considered the framework of the weighted stochastic block model (WSBM; e.g., [36]), which provides a complementary means of quantifying how well a partition fits the data

  • Little is known about the network structure of the scientific ideas themselves, or what features of this network might be most effective at facilitating innovation

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Summary

Introduction

The practice of scientific research represents the collective effort of humans to acquire information, generate insight, and disseminate knowledge. Scientific inquiry has been carried out for centuries, the recent expansion of meta-data collection has allowed a robust body of literature to develop around the scientific study of science itself. This work has led to advances in predicting the success of scientific papers and authors [1, 2], found that articles. The funding agencies had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

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