Deterministic blockmodeling is an approach in blockmodeling that does not assume a probabilistic model, and instead relies on the exact or approximate algorithms, which are used to find blockmodel(s). This approach typically minimizes some inconsistency that can occur 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 the 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]

The opposite approach to deterministic blockmodeling is a stochastic blockmodeling approach.[2]

See also

References

  1. ↑ 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. PMID 25106397. S2CID 35344911.
  2. 1 2 Wyse, Jason; Friel, Nial; Latouche, Pierre (2015). "Inferring structure in bipartite networks using the latent blockmodel and exact ICL": 1–25. arXiv:1404.2911. {{cite journal}}: Cite journal requires |journal= (help)
  3. ↑ 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.
  4. ↑ 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. doi:10.1007/s003579900004. S2CID 122734037.


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