Exercise

# Plot degree centrality on projection

Here, you're going to compare the degree centrality distributions for each of the following graphs: the original graph `G`

, the people graph projection `peopleG`

, and the clubs graph projection `clubsG`

. This will reinforce the difference in degree centrality score computation between bipartite and unipartite versions of degree centrality metrics. The node lists `people`

and `clubs`

have been pre-loaded for you.

Recall from the video that the bipartite functions require passing in a container of nodes, but will return all degree centrality scores nonetheless. Remember also that degree centrality scores are stored as dictionaries (mapping node to score).

Instructions

**100 XP**

- Plot the degree centrality distribution of the original graph
`G`

, using the`degree_centrality`

function from the bipartite module:`nx.bipartite.degree_centrality()`

. It takes in two arguments: The graph`G`

, and one of the node lists (`people`

or`clubs`

). - Plot the degree centrality distribution of the
`peopleG`

graph, using the normal/non-bipartite`degree_centrality`

function from NetworkX:`nx.degree_centrality()`

. - Plot the degree centrality distribution of the
`clubsG`

graph, using the normal/non-bipartite`degree_centrality`

function from NetworkX:`nx.degree_centrality()`

.