Python Tutorial: Bipartite graphs

Want to learn more? Take the full course at https://learn.datacamp.com/courses/intermediate-network-analysis-in-python at your own pace. More than a video, you'll learn hands-on coding & quickly apply skills to your daily work. --- Great work on the first chapter! I hope you’re feeling warmed up for what's coming next! We're going to talk about bipartite graphs here. There are two conditions for defining a bipartite graph. Firstly, it’s a graph in which the nodes are partitioned into two sets. Secondly, the nodes in one set cannot be connected to one another; they can only be connected to nodes in the other set. This is in contrast to the “unipartite” graphs that you’ve been using in the previous course, in which the nodes are not explicitly partitioned into two sets. Let’s use an example to make this concrete. An example where a bipartite graph may come in handy is in the modelling of purchases that a customer makes. In this case, the nodes are partitioned into two sets: the customers partition, and the products partition. Edges denote that a customer has purchased a particular product. In this case, it makes perfect sense that products cannot be connected to one another; after all a product cannot purchase a product. Likewise for customers. How do we include this partition information in NetworkX? Though it is not required by the API, by convention, bipartite information is encoded as part of the node attributes (or metadata), using the “bipartite” keyword. In the toy example above, let’s say I’m modelling the connectivity between a “customers” and an “products” partition. In constructing the graph I can use the graph object's method, add_nodes_from, passing in the list of nodes from each partition as the first argument. By passing in the bipartite=‘customers’ or bipartite=‘products’ argument, the method will automatically create the node's metadata dictionary with the key bipartite and the value product or customers If we inspect the nodes of the graph, wh