Graphs in networkx.md

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To do:	Enter:
Load networkx and matplotlib	import networkx as nx import matplotlib.pyplot as plt
Create an empty graph	G = nx.Graph()
Add an edge to G between nodes 2 and 3	G.add_edge(2,3)
Add several edges to G all at once	G.add_edges_from([(1,3), (2,4), (2,3)])
Print a list of the edges in G	for e in G.edges(): print(e)
Print a list of the nodes in G	for v in G.nodes(): print(v)
Create the graph F from an edge list	friend_list = [("Alice", "Bob"), ("Alice", "Darla"), ("Chuck", "Bob"), ("Bob", "Darla")] F = nx.Graph(friend_list)
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Create the graph F from a dictionary	friends_dict = {"Alice":["Bob", "Darla"], "Bob":["Alice", "Chuck", "Darla"], "Chuck":["Bob"], "Darla":["Alice", "Bob"] } F = nx.Graph(friends_dict)
Create a basic visualization of G	nx.draw(G)
Draw G with the vertices labelled	nx.draw(G, with_labels = True)
Draw G with the vertices labelled and colored yellow	nx.draw(G, with_labels = True, node_color = "yellow")
Draw G in a circular layout	nx.draw_circular(G)
Draw a random graph with 12 vertices and a 50% probability of connection	G = nx.gnp_random_graph(12,0.5) nx.draw(G)
Draw a random graph with 12 vertices and 15 edges	G = nx.gnm_random_graph(12,15) nx.draw(G)
Convert G to an edge list	nx.to_edgelist(G)
Convert G to a dictionary	nx.to_dict_of_lists(G)
Find the number of vertices in G	G.number_of_nodes()
Find the degrees of each vertex in G	nx.degree(G) Returns a dictionary of nodes and degrees.
Find the degree of vertex a in G	d = nx.degree(G) d["a"]
Find all the neighbors of vertex a	neighbors = G.neighbors("a") for n in neighbors: print(n)
Find the number of edges in G	G.number_of_edges()
Create $K_8$, the complete graph on 8 vertices	G = nx.complete_graph(8)
Create $K_{5,3}$, the complete bipartite graph	G = nx.complete_bipartite_graph(5,3)
Create the cycle graph $C_9$	G = nx.cycle_graph(9)
Create the path graph $P_5$	G = nx.path_graph(5)