```
In [17]:
```import networkx as nx
import networkx.algorithms.bipartite as bi

```
In [51]:
```# Instruction from:
# https://networkx.readthedocs.io/en/stable/examples/algorithms/davis_club.html
G = nx.davis_southern_women_graph()
women = G.graph['top']
clubs = G.graph['bottom']
# project bipartite graph onto women nodes
W = bi.projected_graph(G, women)
# project bipartite graph onto women nodes keeping number of co-occurence
# the degree computed is weighted and counts the total number of shared contacts
W = bi.weighted_projected_graph(G, women, ratio=False)

```
In [81]:
```# Investigate the strength of the connections between the women (clubs)
# Weighted edges based on common clubs
# textbook: SNAS pg. 104
weights = [edata['weight'] for f,t,edata in W.edges(data=True)]
plt.figure(figsize = (10,10))
nx.draw_networkx(W, width=weights, edge_color=weights)

```
```

```
In [99]:
```# Find measures to further understand the subnetworks:
# Degree Centrality (nx) - centrality in the network
W_deg = nx.degree_centrality(W)
W_deg = {k:round(v,3) for k, v in W_deg.items()}
def sort_x(x):
sort = sorted(x.iteritems(), key=lambda (k,v):(-v,k))
return sort
W_deg = sort_x(W_deg)
W_deg

```
Out[99]:
```

```
In [37]:
```# Now we'll look at the events instead of the women
G2 = nx.davis_southern_women_graph()
women = G2.graph['bottom']
events = G2.graph['top']
# project bipartite graph onto women nodes
e = bi.projected_graph(G2, women)
# project bipartite graph onto women nodes keeping number of co-occurence
# the degree computed is weighted and counts the total number of shared contacts
e = bi.weighted_projected_graph(G2, women, ratio=False)

```
In [47]:
```e.edges(data=True)
weights=[edata['weight'] for f,t,edata in e.edges(data=True)]
plt.figure(figsize=(10,10))
nx.draw_networkx(e, width=weights, edge_color=weights)

```
```

```
In [91]:
```# Check out the degree (number of edges adjacent each node)
e_degree = nx.degree(e)
e_degree = {k:round(v,1) for k, v in e_degree.items()}
e_degree = sort_x(e_degree)
e_degree

```
Out[91]:
```