Elizabeth Sander and Stefano Allesina
This package contains code to search for and analyze group model structure of signed adjacency networks.
All code to search for network groupings can be found in the CCode folder.
To compile the code, run make from terminal while in the folder. See
CCode/example.sh for an explanation of how to run the algorithm.
We use a Metropolis-Coupled Markov Chain Monte Carlo (MCMCMC) search algorithm with a Gibbs sampler to search for the optimal grouping for a given network. Bayes factors are used for model selection.
Code to create taxonomic records for a species list from the ITIS database
is in the PythonCode folder. Detailed instructions can be found in
PythonCode/README-GetTaxonomy.txt.
Several functions to compare and analyze network groupings can be found in
the RCode folder. MutualInformation.R contains functions to calculate
the similarity between partitions. JackknifeMIs.R calculates the MI
between two partitions relative to the maximum possible MI.
RandomizationPvals.R calculates the p-value associated with a MI
value (here, the p-value is the probability of getting an equal or
higher MI when the group identities in the partitions are randomized).
License: MIT