arbiter
JournalsPricingAccount

An illustrated tutorial on mixed-method social network analysis of embedded network agency

Nataša Pantić et al.

Network Science2026https://doi.org/10.1017/nws.2026.10024article
AJG 1
Weight
0.50

What the paper says

This paper presents an illustrated tutorial for conducting an embedded Mixed-Method Social Network Analysis (MMSNA) to examine the dynamic interplay between human agency and social networks. We draw on an empirical study in education that investigated how teachers enact relational agency within their school networks to support the integration of migrant students. We propose a replicable method and stepwise procedure for designing, implementing and evaluating an embedded MMSNA. While the potential of MMSNA has long been recognized across disciplines, its purpose and operationalization are often underexplained. We illustrate how MMSNA can be used to analyze both network structures and the agency of actors embedded within them, in alignment with specific research objectives and theoretical perspectives.

Open paper page →

Cite this paper

https://doi.org/https://doi.org/10.1017/nws.2026.10024

Or copy a formatted citation

@article{nataša2026,
  title        = {{An illustrated tutorial on mixed-method social network analysis of embedded network agency}},
  author       = {Nataša Pantić et al.},
  journal      = {Network Science},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1017/nws.2026.10024},
}

Paste directly into BibTeX, Zotero, or your reference manager.

Flag this paper

An illustrated tutorial on mixed-method social network analysis of embedded network agency

Flags are reviewed by the Arbiter methodology team within 5 business days.

Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.