问Python:一步一步地实现无标度网络的修正Barabasi-Albert模型。

EN

n=100 #Final number of nodes
ncols = 10 #Number of columns of a 10x10 grid
pos = {i : (i // ncols, (n-i-1) % ncols) for i in G.nodes()} #G=graph

def barabasi_albert_graph(n, m, seed=None):

    if m < 1 or  m >=n:
        raise nx.NetworkXError(\
              "Barabási-Albert network must have m>=1 and m<n, m=%d,n=%d"%(m,n))
    if seed is not None:
        random.seed(seed)

    # Add m initial nodes (m0 in barabasi-speak)
    G=empty_graph(m)
    G.name="barabasi_albert_graph(%s,%s)"%(n,m)
    # Target nodes for new edges
    targets=list(range(m))
    # List of existing nodes, with nodes repeated once for each adjacent edge
    repeated_nodes=[]
    # Start adding the other n-m nodes. The first node is m.
    source=m
    while source<n:
        # Add edges to m nodes from the source.
        G.add_edges_from(zip([source]*m,targets))
        # Add one node to the list for each new edge just created.
        repeated_nodes.extend(targets)
        # And the new node "source" has m edges to add to the list.
        repeated_nodes.extend([source]*m)
        # Now choose m unique nodes from the existing nodes
        # Pick uniformly from repeated_nodes (preferential attachement)
        targets = _random_subset(repeated_nodes,m)
        source += 1
    return G