방법 비교

	Triad Census	Blockmodeling	Dyadic Analysis
분야	Sociology	Sociology	Sociology
계열	Process / pipeline	Process / pipeline	Regression model
기원 연도	1970	1976	1981
창시자	Paul Holland & Samuel Leinhardt	Harrison White, Scott Boorman & Ronald Breiger	Holland & Leinhardt (p1); Kenny (Social Relations Model)
유형	Enumeration of the 16 isomorphism classes of directed triads	Network partitioning into positions and a reduced role structure	Analysis of the dyad as the unit, decomposing relational effects
원전	Holland, P. W., & Leinhardt, S. (1970). A method for detecting structure in sociometric data. American Journal of Sociology, 76(3), 492–513.	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.
별칭	triad count, triadic census, 16-type triad census, MAN triad census	block modeling, blockmodel analysis, generalized blockmodeling, CONCOR	dyad analysis, dyadic data analysis, social relations model, dyad census
관련	4	4	4
요약	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.	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.