Multiplex coupling: Connecting the intellectual base and the topics of a research field
Abstract
Two documents can be close because they cite the same works or because they write about the same things, and the two kinds of proximity often disagree. Science mapping reads them in separate maps or merges them, as hybrid clustering does; either way the disagreement is lost. We introduce multiplex coupling, a framework that couples the same documents by their references and by their terms, keeps the two layers separate and analyses their relation. Similarities are standardised against a null model, and the agreement between the layers is measured on random pairs of documents. Consensus clustering partitions each layer into roots, groups sharing an intellectual base, and themes, groups sharing a topic. A test on the roots × themes table identifies branching roots and convergent themes, and pairs of roots are followed over time. Every structure is reported with its persistence across numbers of neighbours, which on synthetic data identifies most false branching. Applications to Web of Science, Scopus and OpenAlex collections show that the two layers carry largely complementary information, and reveal substantive and methodological bases that have grown into several topics. The framework is implemented in bibliometrix.
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BibTeX
@misc{aria2026multiplex,
title = {{Multiplex coupling: Connecting the intellectual base and the topics of a research field}},
author = {Massimo Aria and Corrado Cuccurullo and Maria Spano},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.21858},
eprint = {2610.21858},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.21858},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Multiplex coupling: Connecting the intellectual base and the topics of a research field
AU - Massimo Aria
AU - Corrado Cuccurullo
AU - Maria Spano
PY - 2026
DA - 2026/10/15
PB - arXiv
DO - 10.48550/arxiv.2610.21858
UR - https://arxiv.org/abs/2610.21858
AB - Two documents can be close because they cite the same works or because they write about the same things, and the two kinds of proximity often disagree. Science mapping reads them in separate maps or merges them, as hybrid clustering does; either way the disagreement is lost. We introduce multiplex coupling, a framework that couples the same documents by their references and by their terms, keeps the two layers separate and analyses their relation. Similarities are standardised against a null model, and the agreement between the layers is measured on random pairs of documents. Consensus clustering partitions each layer into roots, groups sharing an intellectual base, and themes, groups sharing a topic. A test on the roots × themes table identifies branching roots and convergent themes, and pairs of roots are followed over time. Every structure is reported with its persistence across numbers of neighbours, which on synthetic data identifies most false branching. Applications to Web of Science, Scopus and OpenAlex collections show that the two layers carry largely complementary information, and reveal substantive and methodological bases that have grown into several topics. The framework is implemented in bibliometrix.
ER -