Python Tutorial : Types of graphs

Key Takeaways

great work we're now going to go a little bit deeper into the network X API and introduce a few more concepts that you can use in network analysis Network X allows us to model different types of graphs for example there are social graphs like Facebook which are undirected graphs undirected graphs are named as such because they are comprised of edges that don't have any inherent directionality associated with them with Facebook for example when one user befriends another the two are automatically connected with an edge this is most commonly drawn as aligned with no arrows between two circles if we explore this in the ipython terminal you can instantiate an empty graph in network X using a next graph and ask for its type undirected graphs have the type graph on the other hand Twitter's social graph is a directed Network this is because of the nature of how users interact with one another for example one user may follow another user but that other user may not follow back as such there's an inherent directionality associated with the graph if we explore this in the ipython terminal you can instantiate an empty directed graph in Network X using an XD graph if you query for its type it will return a digraph object we can also have graphs in which there are multiple edges permitted between the nodes for example we may want to model trips between bike sharing stations each trip may be one edge between the pair of stations if we explore this in the ipython terminal we can likewise instantiate a multi graph using a next multitrader graph object sometimes for practical reasons maybe two memory intensive to model multiple edges per pair of nodes and so one may choose to collapse the edges into a single edge that contains a metadata summary of the original for example we may want to collapse these three edges into a single one and give them a weight metadata with the value three indicating that it was originally three edges between the pair of nodes let's go through one final concept the idea of self loops self loops can be used in certain scenarios such as in bike sharing data where trip begins at a station and ends at the same station one of the exercises you will encounter will leverage what you've learned so far about the network X API to find edges that are self loops in a graph alright let's move on to the exercises now