15.4: Visualizing Networks with NetworkX

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NetworkX also provides functions for visualizing networks. They are not as powerful as other more specialized software¹, but still quite handy and useful, especially for small- to mid-sized network visualization. Those visualization functions depend on the functions defined in matplotlib (pylab), so we need to import it before visualizing networks.

The simplest way is to use NetworkX’s draw function:

$Code 15.11.png$

The result is shown in Fig. 15.4.1. Red circles represent the nodes, and the black lines connecting them represent the edges. By default, the layout of the nodes and edges is automatically determined by the Fruchterman-Reingold force-directed algorithm [62] (called “spring layout” in NetworkX), which conducts a pseudo-physics simulation of the movements of the nodes, assuming that each edge is a spring with a fixed equilibrium distance. This heuristic algorithm tends to bring groups of well-connected nodes closer to each other, making the result of visualization more meaningful and aesthetically more pleasing.

Fig 15.3.png — Figure $\PageIndex{1}$: Visual output of Code 15.11. Zachary’s Karate Club graph is visualized. Your result may not look like this because the spring layout algorithm uses random initial positions by default.

There are several other layout algorithms, as shown in Code 15.12 and Fig.15.5.1. Also, there are many options you can use to customize visualization results (see Code 15.13 and Fig. 15.4.3). Check out NetworkX’s online documentation to learn more about what you can do.

$Code 15.12.png$