MATLAB for Blockmodel Entropy Significance Test
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BESTest.m
DCSBMentropy.m
README.md
SBMentropy.m
SBMlikelihood.m
lazega.mat
lazegaLawyersDemo.m
shuffle.m

README.md

MATLAB for Blockmodel Entropy Significance Test (BESTest)

May 3, 2017 Described in the paper:

"The ground truth about metadata and community detection." Peel, Larremore, Clauset. Science Advances, 2017.

Data and other code: http://danlarremore.com/metadata

Comments or questions to larremore@santafe.edu

Usage:

BESTest.m should be called as p = BESTest(adjMtx,partition,nSamples,modelName)

OUTPUT

  • p - p value as described in the paper

INPUTS

  • adjMtx - NxN undirected network adjacency matrix. Code will check to ensure that the matrix is either symmetric or triangular.
  • partition - Nx1 or 1xN vector in which partition(i) is an integer that enumerates which group vertex i belongs to.
  • nSamples - number of samples used to compute p. Recommended 10k or higher for confident results.
  • modelName - string with desired model. Four options:
    • 'SBMpoisson'
    • 'dcSBMpoisson'
    • 'SBMbernoulli'
    • 'dcSBMmultinomial'

Example Code:

see lazegaLawyersDemo.m for sample code and usage, fully reproducing Table 1 from the manuscript