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#General#

• start the terminal
• navigate to your BinoX main folder
• BinoX can be called with the following command:

  #!bash
  BinoX [OPTIONS]
  

##Arguments## * -v, display version

#!bash
BinoX -v

#!bash
BinoX -h

NOTE: More information about the data format can be found in section Input Data

#Standard Analysis#

If a small network file is given, one can do the randomization step and the evaluation step at once. However the authors advise to separate those two procedures and pre process the network and evaluate the group files afterwards.

#!bash
BinoX -n [network file path] -a [group file path] -b [group file path]

• 1 Network file
• 1-2 Group files

##Arguments##

• -n, defines the path to the network file
• -a, defines the path to file of groupA

##Options## * -b [default = groupA], defines the path to a second group file, groupB. * -c [default = 7], sets the cut off for the link weigth. All links with a score >= [cutoff] will be used for analysis. * -i [default = 100], defines the number of iterations for the monte carlo sampling method. * -m [default = SecondOrder], defines the network randomization method * [SecondOrder], Best effort randomization of the original network. Attempts to conserve second-order assortativity. * [LinkSwap], Swap links as suggested by maslov and sneppen: link pair (a, b) and (c, d) become * [LabelSwap], Permutates node labels that fall into the same ln(deg) bin. * [Assignment], Best effort randomization of the original network. * -g [default = 5], minimum number of nodes per group. a group will be excluded if it has less than -g nodes * -o [default = name of groupfile A_VS_name of groupfile B], output file path and name * -p [default = compact], data format. Optional the format can be set to "large". In that case the program saves all values which were used for calculating the results. * -r filename of preprocessed network. If set BinoX will save the randomized network which was used for analysis.

##Output##

• 1 Result file, BinoX sums up the statistical estimations in a result file

• if the argument -r is defined it will also create a file which represents the pre processed network.

#Creade a pre processed network#

Since the sampling is based on a monte carlo approach, BinoX needs to randomize the network a sufficient amount(defined by the option -i ) of times. As higher -i is set, as more randomizations we have, and more the results will be more accurate. Note that the accuracy is converting with an increasing -i, depending on the topology of the network.

#!bash
BinoX -n [network file path]

• 1 Network file

##Arguments## * -n, defines the path to the network file

##Options## * -c [default = 7], sets the cut off for the link weigth. All links with a score >= [cutoff] will be used for analysis. * -i [default = 100], defines the number of iterations for the monte carlo sampling method. * -m [default = SecondOrder], defines the network randomization method * [SecondOrder] Best effort randomization of the original network. Attempts to conserve second-order assortativity. * [LinkSwap] Swap links as suggested by maslov and sneppen: link pair (a, b) and (c, d) become * [LabelSwap] Permutates node labels that fall into the same ln(deg) bin. * [Assignment] Best effort randomization of the original network. * -r [default = BinoX-NetworkFileName.randNet], Filename of preprocessed network.

#Analysis using a pre processed network# Using a BinoX pre processed network, described here, can speed up the analysis exponentially.

#!bash
BinoX -r [pre processed network file path] -a [group file path] -b [group file path]

• 1 Pre processed network file
• 1-2 Group files

##Arguments## * -r, defines the path to the pre processed network file * -a, defines the path to file of groupA

##Options## * -b, [default = groupA] defines the path to a second group file, groupB. * -g, [default = 5]minimum number of nodes per group. a group will be excluded if it has less than -g nodes * -o, [default = name of groupfile A_VS_name of groupfile B] output file path and name * -p, [default = compact] Data format. Optional the format can be set to "large". In that case the program saves all values which were used for calculating the results.