Mapping the intellectual landscape of digital social networks: a bibliometric and citation network analysis

Abstract Network science is an interdisciplinary field that transcends traditional academic boundaries. However, a critical gap exists in understanding the epistemological fragmentation between empirical sociology and algorithmic science. This study addresses this by conducting a comparative bibliometric analysis of three leading journals: Social Networks, Network Science, and the Journal of Complex Networks. Beyond traditional mapping, our central contribution is the identification of ’topological bottlenecks’—seminal works that gatekeep interdisciplinary knowledge flow. We reveal a hierarchical ’scale-free’ structure that, while ensuring theoretical continuity, may inadvertently stifle the development of new frameworks needed for modern algorithmic complexities.

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Publication Details

Journal
Social Network Analysis and Mining
Published
2026-06-27
DOI
https://doi.org/10.1007/s13278-026-01617-0
Primary Topic
Complex Network Analysis Techniques
Type
article
Field-Weighted Citation Impact
0.00
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article

Mapping the intellectual landscape of digital social networks: a bibliometric and citation network analysis

Pu Cui
Social Network Analysis and Mining
Complex Network Analysis Techniques
article

Mapping the intellectual landscape of digital social networks: a bibliometric and citation network analysis

Pu Cui
article en

Abstract

Abstract Network science is an interdisciplinary field that transcends traditional academic boundaries. However, a critical gap exists in understanding the epistemological fragmentation between empirical sociology and algorithmic science. This study addresses this by conducting a comparative bibliometric analysis of three leading journals: Social Networks, Network Science, and the Journal of Complex Networks. Beyond traditional mapping, our central contribution is the identification of ’topological bottlenecks’—seminal works that gatekeep interdisciplinary knowledge flow. We reveal a hierarchical ’scale-free’ structure that, while ensuring theoretical continuity, may inadvertently stifle the development of new frameworks needed for modern algorithmic complexities.

Social Network Analysis and Mining
University of California San Diego (US)
Openalex Percentile: Top 6%
Complex Network Analysis Techniques
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