April 14, 2018

**Undirected** - generally used for symmetric relationships, these graphs have edges between nodes that do not have an orientation (or direction). An example could be a family and friends network — if one person is a family member of another, we can assume that the same is true in the opposite direction.

**Directed** - these are useful for asymmetric relationships. The edges on these graphs come with a particular direction from one node to the next. These are useful when there is a specific relationship between two nodes, such as an animal ecosystem food network where one animal is the preditor and one is the prey.

**Weighted** - these have a numerical value assigned to the edge. The weight of an edge can be different for different connections between nodes. This is good if you are trying to count some relationship between two nodes, such as the number of emails sent from one person node to another person node.

**Signed** - this provides a binary classification of edges between nodes. Each edge has a positive or negative sign, which could denote things like ‘friend’ or ‘enemy’ between two nodes.

**Labeled** - edges have attributes (labels, colors) to describe the type of edge being used to connect two nodes. For example, describing the nature of the relationship between two nodes in a categorical fashion.

**Multi-graph** - labeled with multiple edges connecting two nodes. If there are two or more types of relationships between two nodes, there could be multiple edges connecting them. This is useful for Labeled graphs where the relationship between two nodes is defined by more than one category.

```
import math
import networkx as nx
import pandas as pd
import numpy as np
from networkx.algorithms import bipartite
import matplotlib.pyplot as plt
%matplotlib notebook
```

```
def plot_graph(G, weight_name=None):
'''
G: a networkx G
weight_name: name of the attribute for plotting edge weights (if G is weighted)
'''
%matplotlib notebook
import matplotlib.pyplot as plt
fig = plt.figure(figsize=(8,8))
fig.suptitle(weight_name)
pos = nx.spring_layout(G)
edges = G.edges()
weights = None
plt.axis('off')
if weight_name:
weights = [int(G[u][v][weight_name]) for u,v in edges]
labels = {i:k for i, k in nx.get_edge_attributes(G, weight_name).items() if k != 0}
nx.draw_networkx_edge_labels(G,pos,edge_labels=labels)
nx.draw_networkx(G, pos, edges=edges, width=weights, alpha=0.7);
else:
nx.draw_networkx(G, pos, edges=edges, alpha=0.7);
```

The challenge set in front of me is to determine if there is a correlation between employees movie interests and their relationship with one another. I have data that measures employee relationships, and I have data that recorded the movies each employee has watched. The plan is to correlate the employee relationship attributes with the shared movie interest attributes.

```
# Load in employees and movies as graph -- edges connect movies seen by employees
emp_movDF = pd.read_csv('Employee_Movie_Choices.txt',sep='\t',engine='python')
emp_movG = nx.from_pandas_dataframe(emp_movDF, *emp_movDF.columns)
# Look at all the different graph layout styles
pos_options = [x for x in nx.__dir__() if x.endswith('_layout')]
plot_count = len(pos_options)
fig = plt.figure(figsize=(plot_count,plot_count*4))
for i, p in enumerate(pos_options):
pos = getattr(nx, p)(emp_movG)
ax = fig.add_subplot(plot_count, 1, i+1)
ax.set_title(p)
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
nx.draw_networkx(emp_movG, pos, ax=ax)
plt.tight_layout()
plt.show()
```

`<IPython.core.display.Javascript object>`

```
G = nx.Graph()
G.add_node('Josh')
G.add_edge('Josh', 'Andrew', {'relationship':'friends', 'weight':10})
G.add_edge('Josh', 'Mike', {'relationship':'friends', 'weight':7})
G.add_edge('Josh', 'Hochiang', {'relationship':'friends', 'weight':7})
G.add_edge('Josh', 'Christina', {'relationship':'friends', 'weight':5})
G.add_edge('Josh', 'Lauren', {'relationship':'friends', 'weight':4})
plot_graph(G, weight_name='weight')
```

`<IPython.core.display.Javascript object>`

```
# Get left and right bipartite nodes
mov_nodes = bipartite.sets(emp_movG)[0]
emp_nodes = bipartite.sets(emp_movG)[1]
print('List of Movies','\n---------------')
for i in mov_nodes: print(i)
print()
print('List of Employees','\n----------------')
for i in emp_nodes: print(i)
print()
print('Employee Movie Choices file','\n----------------------')
!cat Employee_Movie_Choices.txt
print()
print('Employee Relationship file','\n----------------------')
!cat Employee_Relationships.txt
```

```
List of Movies
---------------
Mean Girls
The Dark Knight
Monty Python and the Holy Grail
The Matrix
The Shawshank Redemption
The Godfather
Forrest Gump
The Social Network
Anaconda
Kung Fu Panda
Snakes on a Plane
List of Employees
----------------
Joan
Claude
Lee
Vincent
Pablo
Frida
Georgia
Andy
Employee Movie Choices file
----------------------
#Employee Movie
Andy Anaconda
Andy Mean Girls
Andy The Matrix
Claude Anaconda
Claude Monty Python and the Holy Grail
Claude Snakes on a Plane
Frida The Matrix
Frida The Shawshank Redemption
Frida The Social Network
Georgia Anaconda
Georgia Monty Python and the Holy Grail
Georgia Snakes on a Plane
Joan Forrest Gump
Joan Kung Fu Panda
Joan Mean Girls
Lee Forrest Gump
Lee Kung Fu Panda
Lee Mean Girls
Pablo The Dark Knight
Pablo The Matrix
Pablo The Shawshank Redemption
Vincent The Godfather
Vincent The Shawshank Redemption
Vincent The Social Network
Employee Relationship file
----------------------
Andy Claude 0
Andy Frida 20
Andy Georgia -10
Andy Joan 30
Andy Lee -10
Andy Pablo -10
Andy Vincent 20
Claude Frida 0
Claude Georgia 90
Claude Joan 0
Claude Lee 0
Claude Pablo 10
Claude Vincent 0
Frida Georgia 0
Frida Joan 0
Frida Lee 0
Frida Pablo 50
Frida Vincent 60
Georgia Joan 0
Georgia Lee 10
Georgia Pablo 0
Georgia Vincent 0
Joan Lee 70
Joan Pablo 0
Joan Vincent 10
Lee Pablo 0
Lee Vincent 0
Pablo Vincent -20
```

```
# Assign node attributes (employees and movies)
nx.set_node_attributes(emp_movG, 'type', {i:'movie' for i in mov_nodes})
nx.set_node_attributes(emp_movG, 'type', {i:'employee' for i in emp_nodes})
```

```
# Get Projected graph of employees, seeing what movie similaries there are
emp_projected = bipartite.weighted_projected_graph(emp_movG, emp_nodes)
plot_graph(emp_projected, 'weight')
```

`<IPython.core.display.Javascript object>`

```
# Load in regular graph - Relationships
relDF = pd.read_csv('Employee_Relationships.txt', sep='\t', names=['e1', 'e2', 'relationship'])
relG = nx.from_pandas_dataframe(relDF, *relDF.columns)
max_weight = max(relDF['relationship'])
plt.figure('Weighted Relationships between coworkers', figsize=(8,8))
pos = nx.spring_layout(relG)
edges = relG.edges()
plt.axis('off')
friends = ['green' if x[2]['relationship'] > 0 else 'red' for x in relG.edges(data=True)]
weights = [int(relG[u][v]['relationship'])**2/max_weight**2 * 10 for u,v in edges]
labels = nx.get_edge_attributes(relG, 'relationship')
# nx.draw_networkx_edge_labels(relG,pos, edge_labels=labels)
nx.draw_networkx(relG, pos, edges=edges, width=weights, alpha=0.7, edge_color=friends, node_color=node_color);
plt.show()
```

`<IPython.core.display.Javascript object>`

```
# Add shared movie interest attributes from projected graph
weight_attr = {(i[0], i[1]): i[2] for i in emp_projected.edges(data='weight')}
nx.set_edge_attributes(relG, 'shared_movies', 0)
nx.set_edge_attributes(relG, 'shared_movies', weight_attr)
plot_graph(relG, 'shared_movies')
```

`<IPython.core.display.Javascript object>`

```
# Create two column DF on the attributes of graph edges
idx = [(i[0], i[1]) for i in relG.edges(data=True)]
rel = [i[2] for i in relG.edges(data='relationship')]
mov = [i[2] for i in relG.edges(data='shared_movies')]
corrDF = pd.DataFrame({'relationship':rel, 'shared_movies':mov}, index=idx)
print('Final Correlation Between relationships and movie interests:')
# Determine correlation between relationships and shared movies
corrDF.corr('pearson')
```

`Final Correlation Between relationships and movie interests:`

relationship | shared_movies | |
---|---|---|

relationship | 1.000000 | 0.788396 |

shared_movies | 0.788396 | 1.000000 |

Unique Graph structure where nodes can be split into a left node subgroup and a right node subgroup, where the left nodes are connected only to the right nodes and visa versa. For example, the defensive positions on a football team may be connected to a particular offensive position, but no defensive position is assigned to guard another defensive position.

`from networkx.algorithms import bipartite`

```
B = nx.Graph()
B.add_nodes_from(['A','B', 'C', 'D'], bipartite=0)
B.add_nodes_from([1,2,3,4], bipartite=1)
B.add_edges_from([('A',1), ('B',2), ('C',3), ('D',4), ('D', 2), ('A', 3)])
bipartite.is_bipartite(B)
```

`True`

`bipartite.sets(B)`

`({1, 2, 3, 4}, {'A', 'B', 'C', 'D'})`

Within the subgroups of Bipartitite Graphs, there can be Projected Graphs describing the relationship amongst the left and right Bipartite subgroups. For example, in a Bipartite Graph describing fans and their favorite sports teams (left and right Bipartite), you could create a Projected Graph on the fans describing which fans support the same teams, where the edges between fans desribes the number or category of team two fan nodes have in common. These kind of relationships can be useful for viral marketing, finding similarities amongst individuals and customizing marketing material accordingly.

```
X1 = set(['A', 'B', 'C', 'D'])
P = bipartite.weighted_projected_graph(B, X1)
print('Described share connections amongst L-group in Bipartite Graph')
P.edges(data=True)
```

```
Described share connections amongst L-group in Bipartite Graph
[('B', 'D', {'weight': 1}), ('C', 'A', {'weight': 1})]
```

Existing connections indicate a high probability of where a new connection will be added — two nodes that share a connection have a high probability of becoming connected themselves/

Local Clustering Coefficient, which can be computed on a node by the number of pairs of connections divided by the number of pairs of connections from the nodes connections.

Global Clustering Coefficient, which can be described by taking the average of all the local clustering coefficients from each node, or by transiity — number of triangles divided by the number of open triads