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# Erdos Renyi

Create an G{n,m} random graph with n nodes and m edges and report some properties.

This graph is sometimes called the Erdős-Rényi graph but is different from G{n,p} or binomial_graph which is also sometimes called the Erdős-Rényi graph.

    node degree clustering
    0 4 0.3333333333333333
    1 5 0.3
    2 4 0.16666666666666666
    3 4 0.5
    4 4 0.16666666666666666
    5 2 0
    6 2 0
    7 5 0.3
    8 5 0.2
    9 5 0.3

    the adjacency list
    0 8 2 9 1
    1 2 4 9 3
    2 7 6
    3 9 8 7
    4 7 6 8
    5 8 9
    6
    7 9 8
    8
    9

    import matplotlib.pyplot as plt
    import networkx as nx

    n = 10  # 10 nodes
    m = 20  # 20 edges
    seed = 20160  # seed random number generators for reproducibility

    # Use seed for reproducibility
    G = nx.gnm_random_graph(n, m, seed=seed)

    # some properties
    print("node degree clustering")
    for v in nx.nodes(G):
        print(f"{v} {nx.degree(G, v)} {nx.clustering(G, v)}")

    print()
    print("the adjacency list")
    for line in nx.generate_adjlist(G):
        print(line)

    pos = nx.spring_layout(G, seed=seed)  # Seed for reproducible layout
    nx.draw(G, pos=pos)
    plt.show()

Total running time of the script: (0 minutes 0.040 seconds)

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