Mapping the Intellectual Structure of Social Network Research: A Comparative Bibliometric Analysis
Pengjia Cui, Yawen Dong
TL;DR
This bibliometric study analyzes three leading journals—Social Networks, Journal of Complex Networks, and Network Science—to map the intellectual structure of social network research. By integrating performance analysis, science mapping, and network analysis on Web of Science data, it identifies key authors, influential papers, thematic clusters, and collaboration patterns. The findings reveal distinct disciplinary orientations (empirical/social vs. mathematical/computational vs. interdisciplinary) with varying co-citation and co-authorship structures, highlighting convergence points and opportunities for interdisciplinarity. The work underscores the evolving methodological landscape, including a shift toward multilayer and dynamic network models, and discusses implications for research policy, collaboration, and the advancement of network science across domains.
Abstract
Network science is an interdisciplinary field that transcends traditional academic boundaries, offering profound insights into complex systems across disciplines. This study conducts a bibliometric analysis of three leading journals, Social Networks, Network Science, and the Journal of Complex Networks, each representing a distinct yet interconnected perspective within the field. Social Networks focuses on empirical and theoretical advancements in social structures, emphasizing sociological and behavioral approaches. Network Science bridges physics, computer science, and applied mathematics to explore network dynamics in diverse domains. The Journal of Complex Networks, by contrast, is dedicated to the mathematical and algorithmic foundations of network theory. By employing co-authorship and citation network analysis, we map the intellectual landscape of these journals, identifying key contributors, influential works, and structural trends in collaboration. Through centrality measures such as degree, betweenness, and eigenvector centrality, we uncover the most impactful publications and their roles in shaping the discourse within and beyond their respective domains. Our analysis not only delineates the disciplinary contours of network science but also highlights its convergence points, revealing the evolving trajectory of this dynamic and rapidly expanding field.