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	Blockmodeling	Dyadic Analysis	Analisi delle Reti Sociali
Campo	Sociology	Sociology	Analisi delle reti
Famiglia	Process / pipeline	Regression model	Machine learning
Anno di origine	1976	1981	1934 (sociometry); 1994 (modern formalization)
Ideatore	Harrison White, Scott Boorman & Ronald Breiger	Holland & Leinhardt (p1); Kenny (Social Relations Model)	Moreno, J.L.; formalized by Wasserman & Faust
Tipo	Network partitioning into positions and a reduced role structure	Analysis of the dyad as the unit, decomposing relational effects	Structural/relational analysis framework
Fonte seminale	White, H. C., Boorman, S. A., & Breiger, R. L. (1976). Social structure from multiple networks. I. Blockmodels of roles and positions. American Journal of Sociology, 81(4), 730–780.	Holland, P. W., & Leinhardt, S. (1981). An exponential family of probability distributions for directed graphs. Journal of the American Statistical Association, 76(373), 33–50.	Wasserman, S. & Faust, K. (1994). Social Network Analysis: Methods and Applications. Cambridge University Press. ISBN: 978-0-521-38707-1
Alias	block modeling, blockmodel analysis, generalized blockmodeling, CONCOR	dyad analysis, dyadic data analysis, social relations model, dyad census	SNA, network analysis, sociometric analysis, relational analysis
Correlati	4	4	5
Sintesi	Blockmodeling is a family of methods that simplify a social network by partitioning its actors into positions — groups of actors who are equivalent in their pattern of ties — and summarizing the relations between positions as a compact image, or reduced role structure. Introduced by Harrison White, Scott Boorman, and Ronald Breiger in 1976, it shifts attention from individuals to the structural roles they occupy.	Dyadic analysis treats the dyad — the pair of actors and the relation between them — as the unit of analysis, separating the relational outcome into what each actor brings to all their relationships and what is unique to the specific pair. It spans the descriptive dyad census of network analysis and statistical frameworks such as Holland and Leinhardt's p1 model and Kenny's Social Relations Model, all of which respect the structural non-independence inherent in relational data.	Social Network Analysis (SNA) is a structural method that maps and measures relationships and flows between people, groups, organizations, or other entities modeled as nodes connected by ties (edges). Rather than focusing on individual attributes, SNA reveals how the pattern of connections shapes behavior, influence, information flow, and outcomes within a system.