🤖 AI Summary
This study investigates the knowledge structure and interdisciplinary evolution of social network research. Methodologically, it constructs co-authorship and citation dual networks by systematically comparing three authoritative journals—*Social Networks*, *Network Science*, and *Journal of Complex Networks*—and applies multidimensional centrality measures (degree, betweenness, and eigenvector centrality) to identify pivotal scholars, foundational publications, and bridging journals. It proposes, for the first time, a multi-centrality-based approach to mapping cross-domain knowledge structures, enabling precise characterization of disciplinary boundaries and integration nodes. Results reveal a three-dimensional evolutionary trajectory in network science: from social empiricism → dynamical modeling → mathematical foundations. This provides empirical evidence and methodological support for understanding knowledge convergence mechanisms and the dynamic reconfiguration of disciplinary boundaries.
📝 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.