`skimage.graph._graph:central_pixel`

source: /dev/scikit-image/src/skimage/graph/_graph.py :163

Signature

   def     central_pixel (    graph    ,    nodes    =  None   ,    shape    =  None   ,    partition_size    =  100     )

Find the pixel with the highest closeness centrality.

Closeness centrality is the inverse of the total sum of shortest distances from a node to every other node.

graph : scipy.sparse.csr_array or scipy.sparse.csr_matrix: The sparse representation of the graph.
nodes : array of int: The raveled index of each node in graph in the image. If not provided, the returned value will be the index in the input graph.
shape : tuple of int: The shape of the image in which the nodes are embedded. If provided, the returned coordinates are a NumPy multi-index of the same dimensionality as the input shape. Otherwise, the returned coordinate is the raveled index provided in nodes.
partition_size : int: This function computes the shortest path distance between every pair of nodes in the graph. This can result in a very large (N*N) matrix. As a simple performance tweak, the distance values are computed in lots of partition_size, resulting in a memory requirement of only partition_size*N.

position : int or tuple of int: If shape is given, the coordinate of the central pixel in the image. Otherwise, the raveled index of that pixel.
distances : array of float: The total sum of distances from each node to each other reachable node.