Introduction to the Modeling and Analysis of Complex Systems

15.3. CONSTRUCTING NETWORK MODELS WITH NETWORKX 303Multigraph A graph that may contain multiple edges. Many mathematicians alsoallow multigraphs to contain self-loops. Multigraphs can be undirected or directed.Hyperedge A generalized concept of an edge that can connect any number of nodesat once, not just two. A graph made of hyperedges is called a hypergraph (notcovered in this textbook).According to these taxonomies, all the examples shown in Fig. 15.2 are simple graphs.But many real-world networks can and should be modeled using directed, weighted,and/or multiple edges.Exercise 15.5 Discuss which type of graph should be used to model each of thefollowing networks. Make sure to consider (a) edge directedness, (b) presence ofedge weights, (c) possibility of multiple edges/self-loops, (d) possibility of connectionsamong three or more nodes, and so on.• Family trees/genealogies• Food webs among species• Roads and intersections• Web pages and links among them• Friendships among people• Marriages/sexual relationships• College applications submitted by high school students• Email exchanges among coworkers• Social media (Facebook, Twitter, Instagram, etc.)15.3 Constructing Network Models with NetworkXNow that we have finished the above crash course on graph theoretic terminologies, it istime to begin with computational modeling of networks. As briefly previewed in Sections5.4 and 12.2, there is a wonderful Python module called NetworkX [27] for network modelingand analysis. It is a free network analysis toolkit widely used by network researchers.If you are using Anaconda, NetworkX is already installed. If you are using Enthought