Updating the Complex Systems Keyword Diagram Using Collective Feedback and Latest Literature Data

TL;DR

The paper tackles the problem of an outdated, subjectively crafted complex systems keyword diagram by building a data-driven, weighted keyword association network from collective feedback, recent literature, and OpenAlex-based metrics. It combines manual curation with quantitative relevance calculations to construct edge weights, then uses network visualization and the Louvain method to identify four intertwined communities and a central core of core topics. Key findings show substantial gaps between public perception and scholarly usage, revealing both distinct topical axes and a highly interconnected topic space. The work delivers an up-to-date, data-backed topic map for complex systems and emphasizes reproducibility and potential interactivity for ongoing community-driven refinement.

Abstract

The complex systems keyword diagram generated by the author in 2010 has been used widely in a variety of educational and outreach purposes, but it definitely needs a major update and reorganization. This short paper reports our recent attempt to update the keyword diagram using information collected from the following multiple sources: (a) collective feedback posted on social media, (b) recent reference books on complex systems and network science, (c) online resources on complex systems, and (d) keyword search hits obtained using OpenAlex, an open-access bibliographic catalogue of scientific publications. The data (a), (b) and (c) were used to incorporate the research community's and other public communities' perceptions of the relevant topics, whereas the data (d) was used to obtain more objective measurements of the keywords' relevance and associations from publications made in complex systems science. Results revealed differences and overlaps between public perception and actual usage of keywords in publications on complex systems. Four topical communities were obtained from the keyword association network, although they were highly intertwined with each other. We hope that the resulting network visualization of complex systems keywords provides a more up-to-date, accurate topic map of the field of complex systems as of today.

Figures (4)

• Figure 1: Keyword rankings. Left: Keyword ranking based on the frequency of mentions in datasets (a)-(c) (top; only keywords with three or more mentions were listed; a total of 73 keywords) and its word cloud visualization (bottom; including all keywords). Right: Keyword ranking based on the relevance score obtained using dataset (d) (top; only the top 73 keywords) and its word cloud visualization (bottom; including all keywords).
• Figure 2: Community structure detected in the keyword association network using the Louvain modularity maximization method. Four communities were detected, which can roughly be interpreted as: [Orange, top left] nonlinear dynamics, [Yellow, top right] computational modeling, [Purple, bottom left] biological/ecological/evolutionary/learning/social systems, and [Green, bottom right] networks and systems.
• Figure 3: Keyword association network visualized using Mathematica's spring electrical embedding layout algorithm. Nodes are colored using the same colors as in Fig. \ref{['fig:communities']} according to their community memberships. It is clear that the four "communities" detected are actually highly intertwined with each other with no clear separation.
• Figure 4: An interpretation of large-scale gradients of complex systems topics in the keyword association network. One dimension is the gradient between theoretical concepts and actual phenomena/applications (cyan arrow). The other is the gradient between computational methods and dynamical systems analysis (orange arrow). These dimensions are just suggested interpretation of the overall trends and some topics (nodes) may not follow this interpretation.