tmklmed {dBlockmodeling}

Two-Mode Blockmodeling (Structural Equivalence) Heuristic

Description

This function runs two-mode KL-medians for an RO x CO two-mode binary network matrix.

Usage

tmklmed(A, RC, CC, TLIMIT)

Arguments

A

An RO x CO two-mode binary network matrix.

RC

The number of clusters for row objects (1 < RC < RO).

CC

The number of clusters for column objects (1 < CC < CO).

TLIMIT

A desired time limit.

Value

The function returns the following:

• objval - total number of inconsistencies;

• RP - an RO-dimensional vector of row cluser assignements;

• RC - an RC-dimensional vector of column cluser assignements;

• restarts - the number of restarts within the time limit.

Author(s)

Michael Brusco

References

Brusco, M. J., Doreian, P., & Steinley, D. (2019). Deterministic blockmodeling of signed and two-mode networks: a tutorial with psychological examples. British Journal of Mathematical and Statistical Psychology.

Doreian, P., Batagelj, V., & Ferligoj, A. (2004). Generalized blockmodeling of two-mode network data. Social Networks, 26, 29-53. doi:10.1016/j.socnet.2004.01.002

Brusco, M., Stolze, H. J., Hoffman, M., Steinley, D., & Doreian, P. (2018). Deterministic blockmodeling of two-mode binary network data using two-mode KL-median partitioning. Journal of Social Structure, 19, 1-21. Retrieved from: https://www.exeley.com/exeley/journals/journal_of_social_structure/19/1/pdf/10.21307_joss-2018-007.pdf

Examples

# Load the Turning Point Project network (Brusco & Doreian, 2015) data.
data("nyt")

# Run the two-mode blockmodeling heuristic procedure.
res <- tmklmed(nyt, RC = 9, CC = 5, TLIMIT = 1)

# See the results.
res