Mutualism in socio-semantic networks: structural correlates of publication output across ecosystem approaches to health

Abstract

Abstract While existing scientometric research has examined how individual network position and team composition affect citation-based performance, less is known about how the structural configuration of socio-semantic networks relates to collective publication output. We propose that publication output is associated with configurations that foster mutual visibility between research topics (memes) and author communities. Drawing on mutualistic network theory from ecology, we hypothesise that lower bipartite modularity (H1) and higher nestedness (H2) are associated with higher publication output. We analyse 6,406 publications across three ecosystem approaches to health (EcoHealth, One Health, Planetary Health) over 18 approach × period observations (2004–2023), linking author communities to 20 research topics identified by Latent Dirichlet Allocation. Our analyses show an empirical asymmetry: bipartite modularity is negatively and robustly associated with publication output (Spearman ρ = −0.79, p < 0.001; robust across four community detection algorithms and under leave-one-out by approach), whereas weighted nestedness shows a positive but non-significant association ( ρ = +0.39, p = 0.11), in a direction consistent with H2 but without statistical support at the cell level. The modularity result supports the H1 component of the framework: low modularity — author communities touching multiple topics — fosters mutual visibility between authors and themes. Given the observational design, we report these as field-level patterns, not causal mechanisms. The non-significant association for H2, combined with marked algorithmic variability, invites reconsidering how nestedness is operationalised in socio-semantic contexts. By integrating social and semantic dimensions within a mutualistic framework, this approach complements existing scientometric tools.

The authors' abstract, as published at the source. Scientometrics, 2026 · DOI ↗