Deterministic blockmodeling
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Deterministic blockmodeling is approach in blockmodeling, that does not assume probabilistic model, and instead relies on the exact or approximate algorithms, which are used to find blockmodel(s). This approach typical minimizes some inconsistency, that can accure[check spelling] with the ideal block structure.[1] Such analysis is focused on clustering (grouping) of the network (or adjacency matrix), that is obtained with minimizing an objective function, which measures discrepancy from the ideal block structure.[2]
However, some indirect approaches (or methods between direct and indirect approaches, such as CONCOR) do not explicitly minimize inconsistencies or optimize some criterion function. [3]
This approach was popularized in 1970s, due to the presence of two computer packages (CONCOR and STRUCTURE), that were used to "find a permutation of the rows and columns in the adjacency matrix leading to an approximate block structure".[4]
Opposite approach to the deterministic blockmodeling is a stochastic blockmodeling approach.[2]
References
- ^ Brusco, Michael; Doreian, Patrick; Steinley, Douglas; Satornino, Cinthia B. (2013). "Multiobjective blockmodeling for social network analysis". Psychometrika. 78 (3): 498–525. doi:10.1007/S11336-012-9313-1.
- ^ a b Wyse, Jason; Friel, Nial; Latouche, Pierre (2015). "Inferring structure in bipartite networks using the latent blockmodel and exact ICL": 1–25.
{{cite journal}}: Cite journal requires|journal=(help) - ^ Aleš Žiberna, Generalized blockmodeling of valued networks (pospološeno bločno modeliranje omrežij z vrednostmi na povezavah: doktorska disertacija. Ljubljana: Univerza v Ljubljani, Fakulteta za družbene vede, 2007, p. 22. URL: http://www2.arnes.si/~aziber4/blockmodeling/Dissertation-final-corrected.pdf.
- ^ Snijders, Tom A. B.; Nowicki, Krzysztof (1997). "Estimation and Prediction for Stochastic Blockmodels for Graphs with Latent Block Structure". Journal of Classification. 14: 75–100.