Unveiling intrinsic interactions of science and technology in artificial intelligence using a network portrait divergence approach

Kai Meng, Zhichao Ba, Chunying Wang and Gang Li

Journal of Informetrics, 2025, vol. 19, issue 1

Abstract: Artificial Intelligence (AI) is experiencing unprecedented innovation and transformation, potentially attributed to intimate interactions between science and technology (S&T) within the field. To identify S&T linkages and detect intrinsic interactions within AI, this paper introduces a network portrait divergence approach, where S&T knowledge networks are prototyped as two-dimensional network portraits based on graph-invariant probability distributions, and comparing them by coupling network portrait divergence with knowledge content. Specifically, S&T knowledge of AI is first extracted and unified through KeyBERT and word-alignment algorithms. Subsequently, temporal S&T knowledge networks are constructed and visualized as two network portraits: node portraits and edge-weight portraits. Network portrait divergence, an information-theoretic, graph-like measure for comparing networks, is applied to calculate varying S&T portrait divergences. Finally, internal knowledge flows within S&T and dynamic interactions between them are unearthed based on multiscale backbone analysis. Empirical experiments on both synthetic networks (random graph ensembles) and real-world AI datasets underscore the feasibility and reliability of the network portrait divergence approach.

Keywords: Artificial intelligence; Science-technology linkage; Dynamic interaction; Network comparison; Network portrait divergence (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:infome:v:19:y:2025:i:1:s1751157724001421

DOI: 10.1016/j.joi.2024.101630

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