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Chapter 7
Next, we will only add the edge if it is above a certain threshold. This stops us from
adding edges we don't care about—for example, edges with weight 0. By default, our
threshold is 0, so we will be including all edges right now. However, we will use this
parameter later in the chapter. The code is as follows:
if weight >= threshold:
If the weight is above the threshold, we add the two users to the graph
(they won't be added as a duplicate if they are already in the graph):
G.add_node(user1)
G.add_node(user2)
We then add the edge between them, setting the weight to be the computed
similarity:
G.add_edge(user1, user2, weight=weight)
Once the loops have finished, we have a completed graph and we return it from
the function:
return G
We can now create a graph by calling this function. We start with no threshold,
which means all links are created. The code is as follows:
G = create_graph(friends)
The result is a very strongly connected graph—all nodes have edges, although
many of those will have a weight of 0. We will see the weight of the edges by
drawing the graph with line widths relative to the weight of the edge—thicker
lines indicate higher weights.
Due to the number of nodes, it makes sense to make the figure larger to get a clearer
sense of the connections:
plt.figure(figsize=(10,10))
We are going to draw the edges with a weight, so we need to draw the nodes first.
NetworkX uses layouts to determine where to put the nodes and edges, based on
certain criteria. Visualizing networks is a very difficult problem, especially as the
number of nodes grows. Various techniques exist for visualizing networks, but the
degree to which they work depends heavily on your dataset, personal preferences,
and the aim of the visualization. I found that the spring_layout worked quite well,
but other options such as circular_layout (which is a good default if nothing else
works), random_layout, shell_layout, and spectral_layout also exist.
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