Introduction to the Modeling and Analysis of Complex Systems

16.2. SIMULATING DYNAMICS ON NETWORKS 327• State updating takes place simultaneously on all individuals in the network.• Individuals’ states are initially random.Let’s continue to use pycxsimulator.py and implement the simulator code by definingthree essential components—initialization, observation, and updating.The initialization part is to create a model of social network and then assign randomstates to all the nodes. Here I propose to perform the simulation on our favorite KarateClub graph. Just like the CA simulation, we need to prepare two network objects, onefor the current time step and the other for the next time step, to avoid conflicts during thestate updating process. Here is an example of the initialization part:Code 16.1:def initialize():global g, nextgg = nx.karate_club_graph()g.pos = nx.spring_layout(g)for i in g.nodes_iter():g.node[i][’state’] = 1 if random() < .5 else 0nextg = g.copy()Here, we pre-calculate node positions using spring_layout and store the results under gas an attribute g.pos. Python is so flexible that you can dynamically add any attribute toan object without declaring it beforehand; we will utilize this trick later in the agent-basedmodeling as well. g.pos will be used in the visualization so that nodes won’t jump aroundevery time you draw the network.The g.nodes_iter() command used in the for loop above works essentially the sameway as g.nodes() in this context, but it is much more memory efficient because it doesn’tgenerate an actual fully spelled-out list, but instead it generates a much more compactrepresentation called an iterator, an object that can return next values iteratively. Thereis also g.edges_iter() for similar purposes for edges. It is generally a good idea to usenodes_iter() and edges_iter() in loops, especially if your network is large. Finally, inthe last line, the copy command is used to create a duplicate copy of the network.The observation part is about drawing the network. We have already done this manytimes, but there is a new challenge here: We need to visualize the node states in additionto the network topology. We can use the node_color option for this purpose:Code 16.2:def observe():