The clustering coefficient of a node or a vertex in a graph depends on how close the neighbors are so that they form a clique (or a small complete graph), as shown in the following diagram:

There is a well known formula to cluster coefficients, which looks pretty heavy with mathematical symbols. However, to put it in simple words, take a look at the following equation:

This involves keeping track of the links at every vertex and calculating the clustering index at every vertex, where the neighbor of a node in the most obvious sense is a node that is only one link away from that node. Clustering index calculation is shown here:

The following code illustrates how you can show the characters of the Les Miserables novel and how each character is associated or connected to other characters:

import networkx as nx
from pylab import rcParams
rcParams['figure.figsize'] = 12, 12

G = nx.read_gml('/Users/kvenkatr/Downloads/lesmiserables.gml', relabel=True)
G8= G.copy...