Linganisha mbinu

	Triad Census	Dyadic Analysis	Uchambuzi wa Mitandao ya Kijamii
Nyanja	Sociology	Sociology	Uchanganuzi wa Mitandao
Familia	Process / pipeline	Regression model	Machine learning
Mwaka wa asili	1970	1981	1934 (sociometry); 1994 (modern formalization)
Mwanzilishi	Paul Holland & Samuel Leinhardt	Holland & Leinhardt (p1); Kenny (Social Relations Model)	Moreno, J.L.; formalized by Wasserman & Faust
Aina	Enumeration of the 16 isomorphism classes of directed triads	Analysis of the dyad as the unit, decomposing relational effects	Structural/relational analysis framework
Chanzo asilia	Holland, P. W., & Leinhardt, S. (1970). A method for detecting structure in sociometric data. American Journal of Sociology, 76(3), 492–513.	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
Majina mbadala	triad count, triadic census, 16-type triad census, MAN triad census	dyad analysis, dyadic data analysis, social relations model, dyad census	SNA, network analysis, sociometric analysis, relational analysis
Zinazohusiana	4	4	5
Muhtasari	The triad census counts how many of a directed network's three-actor subgroups fall into each of the 16 possible types of triad, providing a compact fingerprint of the network's local structure. Introduced by Paul Holland and Samuel Leinhardt in 1970, it is the standard way to test structural theories — balance, clustering, transitivity, ranked clusters — by comparing the observed distribution of triad types against what a random network would produce.	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.