Social Network Analysis in the Field of Renewable Energy and Biodiversity Research
Year: 2025
Type: dissertation
Abstract: This thesis examines research collaboration patterns and knowledge structures in the interdisciplinary field of renewable energy and biodiversity. As global energy transitions accelerate, understanding how researchers collaborate and share knowledge across disciplinary boundaries becomes increasingly important. Employing scientometric methods and social network analysis, this study provides a comprehensive structural analysis of the field. The analysis is based on 936 academic papers sourced from the OpenAlex database, from which four distinct networks were constructed: author co-authorship, institution co-authorship, author co-citation, and institution co-citation networks. Following rigorous data cleaning and disambiguation procedures, the study computes key network metrics including degree centrality, strength, betweenness centrality, clustering coefficient, and average path length. Community detection algorithms and small-world coefficient tests are applied to uncover underlying structural patterns. Results reveal that both collaboration and citation networks exhibit distinct core-periphery structures. Author networks demonstrate small-world characteristics, indicating efficient information flow despite local clustering. The analysis identifies pivotal scholars, influential institutions, and dominant research communities within the field. Notably, the study reveals a divergence between collaborative activity and academic influence, suggesting that different mechanisms drive social and cognitive structures. These findings contribute to understanding knowledge production dynamics in interdisciplinary research and offer practical insights for research policy and cross-disciplinary collaboration. Keywords: research collaboration, co-authorship network, co-citation network, social network analysis, renewable energy, biodiversity
DOI: 10.5167/uzh-293028
Author: Ao, Denggeyile
Language: English
Subfield: Statistics, Probability and Uncertainty
Field: Decision Sciences
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W7139014523
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