The Emergence of Higher-Order Structure in Scientific and Technological Knowledge Networks
Thomas Gebhart, Russell J. Funk

TL;DR
This paper uses algebraic topology to analyze the development of scientific and technological knowledge networks, revealing rapid growth in higher-order structures that influence innovation and the nature of scientific output.
Contribution
It introduces a novel application of algebraic topology to measure higher-order structures in knowledge networks, uncovering their growth and impact on scientific progress.
Findings
Higher-order structures grow rapidly across fields.
Emergence of higher-order structure correlates with increased innovation.
Higher-order regimes produce more abstract and cumulative scientific knowledge.
Abstract
The growth of science and technology is a recombinative process, wherein new discoveries and inventions are built from prior knowledge. Yet relatively little is known about the manner in which scientific and technological knowledge develop and coalesce into larger structures that enable or constrain future breakthroughs. Network science has recently emerged as a framework for measuring the structure and dynamics of knowledge. While helpful, existing approaches struggle to capture the global properties of the underlying networks, leading to conflicting observations about the nature of scientific and technological progress. We bridge this methodological gap using tools from algebraic topology to characterize the higher-order structure of knowledge networks in science and technology across scale. We observe rapid growth in the higher-order structure of knowledge in many scientific and…
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Taxonomy
TopicsComplex Network Analysis Techniques · Complex Systems and Time Series Analysis · Opinion Dynamics and Social Influence
