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ID_MAPS 2014-02-24
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README 2014-02-24 1.9 kB
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communityDetection.C 2014-02-24 36.3 kB
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edge.h 2014-02-24 2.4 kB
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eigen_sym.h 2014-02-24 5.5 kB
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Headers.h 2014-02-24 481 Bytes
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Helper.h 2014-02-24 3.2 kB
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ludcmp.h 2014-02-24 3.5 kB
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Makefile 2014-02-24 1.8 kB
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node.h 2014-02-24 4.4 kB
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nr3.h 2014-02-24 13.1 kB
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ran.h 2014-02-24 624 Bytes
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readInputFile.h 2014-02-24 9.7 kB
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Totals: 14 Items   83.0 kB 0 
------------------------------------------------------
### Community Detection using Modulairty (2013)    ###
### Suite of Modualrity based community detection  ###
### algorithms                                     ###
### Dr. Colin Mclean <cmclean5@staffmail.ed.ac.uk> ###
------------------------------------------------------


----------------------------
*** TO INSTALL AND BUILD ***
----------------------------

(1) Build clustering package within a convienient location,  by running the Makefile. 

 > make

This should create the executable 'run'.


----------------------------
***       TO RUN         ***
----------------------------

The user can obtain help on running each of the arguments by typing the following:
 > ./run

> ./run can take the following arguments: 
> -file         : the network file to run 
> -fullNetwork  : Use full network and not the lagrest component 
> -searchK      : run algorithm through K space findKlow = 1, findKhigh = N, Kinterval = 1 
> -findKlow     : run algorithm through K, starting at K = findKlow 
> -findKhigh    : run algorithm through K, ending at K = findKhigh 
> -Kinterval    : run algorithm through K with spacing Kinterval 
> -searchLL     : run algorithm with K = findK first calculating the solution's space LLmean 
> -LLiterations : run algorithm with K = findK first calculating the solution's space LLmean, using LLiterations 
> -LLmean       : run algorithm using Log Likelihood mean = LLmean 
> -findK        : run algorithm with K = findK for max Log Likelihood 
> -geneNames    : can use if running -findK over a Human PPI data
> -----
> EXAMPLE 1
> -----
> ./run -searchK -file testData/karate.txt
> ./run -findK 4 -file testData/karate.txt
> -----
> EXAMPLE 2
> -----
> ./run -findKlow 1 -findKhigh 20 -Kinterval 1 -file testData/karate.txt
> ./run -searchLL -findK 4 -file testData/karate.txt
> -----

Some Example data is included in the testData directory
Source: README, updated 2014-02-24