IndeCut evaluates performance of network motif discovery algorithms.Motivation
 In this work, we present IndeCut, the first method to date that characterizes network motif finding algorithm performance in terms of uniform sampling on realistically sized networks.  We demonstrate that it is critical to use IndeCut prior to running any network motif finder for two reasons.  First, IndeCut indicates the number of samples needed for a tool to produce an outcome that is both reproducible and accurate.  Second, IndeCut allows users to choose the tool that generates samples in the most independent fashion for their network of interest among many available options.


 The open source software package is available at https://github.com/megrawlab/IndeCut.



IndeCut
IndeCut evaluates performance of network motif discovery algorithms.Motivation
 In this work, we present IndeCut, the first method to date that characterizes network motif finding algorithm performance in terms of uniform sampling on realistically sized networks.  We demonstrate that it is critical to use IndeCut prior to running any network motif finder for two reasons.  First, IndeCut indicates the number of samples needed for a tool to produce an outcome that is both reproducible and accurate.  Second, IndeCut allows users to choose the tool that generates samples in the most independent fashion for their network of interest among many available options.


 The open source software package is available at https://github.com/megrawlab/IndeCut.



IndeCut