Let's create a network and then try to calculate its centrality metrics. The following code block illustrates this:

import networkx as nx
import matplotlib.pyplot as plt
vertices = range(1,10)
edges = [(7,2), (2,3), (7,4), (4,5), (7,3), (7,5), (1,6),(1,7),(2,8),(2,9)]
G = nx.Graph()
G.add_nodes_from(vertices) 
G.add_edges_from(edges)
nx.draw(G, with_labels=True,node_color='y',node_size=800)

The graph produced by this code is as follows:

So far, we have studied different measures of centrality. Let's calculate them for the preceding example:

Note that the metrics of centrality are expected to give the centrality measure of a particular vertex in a graph or subgraph. Looking at the graph, the vertex labeled 7 seems to have the most central location. Vertex 7 has the highest values in all four metrics of centrality, thus reflecting its importance in this context.

Now let's look into how we can retrieve information from the graphs. Graphs are complex data structures with lots of information stored both in vertices and edges. Let's look at some strategies that can be used to navigate through graphs efficiently in order to gather information from them to answer queries.