# Source code for topologic.statistics.degree_centrality

```# Copyright (c) Microsoft Corporation.
import networkx as nx
import numpy as np
from .defined_histogram import DefinedHistogram
from typing import List, Union
from .make_cuts import MakeCuts, filter_function_for_make_cuts

[docs]def histogram_degree_centrality(
graph: nx.Graph,
bin_directive: Union[int, List[Union[float, int]], np.ndarray, str] = 10
) -> DefinedHistogram:
"""
Generates a histogram of the vertex degree centrality of the provided graph.
Histogram function is fundamentally proxied through to numpy's `histogram` function, and bin selection
follows `numpy.histogram` processes.

:param networkx.Graph graph: the graph.  No changes will be made to it.
:param bin_directive: Is passed directly through to numpy's
"histogram" (and thus, "histogram_bin_edges") functions.
See: https://docs.scipy.org/doc/numpy-1.15.1/reference/generated/numpy.histogram_bin_edges.html#numpy.histogram_bin_edges
In short description: if an int is provided, we use `bin_directive` number of equal range bins.
If a sequence is provided, these bin edges will be used and can be sized to whatever size you prefer
Note that the np.ndarray should be ndim=1 and the values should be float or int.
:type bin_directive: Union[int, List[Union[float, int]], numpy.ndarray, str]
:return: A named tuple that contains the histogram and the bin_edges used in the histogram
:rtype: DefinedHistogram
"""  # noqa:501

degree_centrality_dict = nx.degree_centrality(graph)
histogram, bin_edges = np.histogram(
list(degree_centrality_dict.values()),
bin_directive
)
return DefinedHistogram(histogram=histogram, bin_edges=bin_edges)

[docs]def cut_vertices_by_degree_centrality(
graph: nx.Graph,
cut_threshold: Union[int, float],
cut_process: MakeCuts
) -> nx.Graph:
"""
Given a graph and a cut_threshold and a cut_process, return a copy of the graph with the vertices outside of the
cut_threshold.

:param networkx.Graph graph: The graph that will be copied and pruned.
:param cut_threshold: The threshold for making cuts based on degree centrality.
:type cut_threshold: Union[int, float]
:param MakeCuts cut_process: Describes how we should make the cut; cut all edges larger or smaller than the
cut_threshold, and whether exclusive or inclusive.
:return: Pruned copy of the graph
:rtype: networkx.Graph
"""
graph_copy = graph.copy()
degree_centrality_dict = nx.degree_centrality(graph_copy)
filter_by = filter_function_for_make_cuts(cut_threshold, cut_process)
vertices_to_cut = list(filter(filter_by, degree_centrality_dict.items()))
for vertex, degree_centrality in vertices_to_cut:
graph_copy.remove_node(vertex)

return graph_copy
```