brainconn.clustering.agreement_weighted

agreement_weighted(ci, wts)[source]

D = AGREEMENT_WEIGHTED(CI,WTS) is identical to AGREEMENT, with the exception that each partitions contribution is weighted according to the corresponding scalar value stored in the vector WTS. As an example, suppose CI contained partitions obtained using some heuristic for maximizing modularity. A possible choice for WTS might be the Q metric (Newman’s modularity score). Such a choice would add more weight to higher modularity partitions.

NOTE: Unlike AGREEMENT, this script does not have the input argument BUFFSZ.

Parameters:
  • ci (MxN numpy.ndarray) – set of M (possibly degenerate) partitions of N nodes
  • wts (Mx1 numpy.ndarray) – relative weight of each partition
Returns:

D – weighted agreement matrix
Return type:

NxN numpy.ndarray