BA algorithm

ba.py

from networkx.utils import random_weighted_sample

def barabasi_albert_graph_rwc(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=nx.empty_graph(m)
    G.name="barabasi_albert_graph(%s,%s)"%(n,m)
    # Target nodes for new edges
    targets=list(range(m))
    # 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))
        # Now choose m unique nodes with random sample by degree weight.
        targets = random_weighted_sample(dict(G.degree()), m)
        source += 1
    return G