Compute the multiscale backbone edge probabilities of a NetworkX graph

multiscale_backbone.py

def compute_p(G, weight='weight', p='p'):
    # Undirected
    for node in G:
        k_n = len(G[node])
        if k_n > 1:
            sum_w = sum( G[node][neighbor][weight] for neighbor in G[node] )
            for neighbor in G[node]:
                edge_weight = G[node][neighbor][weight]
                p_ij = float(edge_weight)/sum_w
                G[node][neighbor][p] = (1-p_ij)**(k_n-1)

def alpha_cut(G, alpha, p='p'):
    C = G.copy()
    C.remove_edges_from((u,v) for u,v,d in G.edges(data=True) if d['p'] < alpha)
    return C

In networkx 2.2 should be:
C.remove_edges_from((u,v) for u,v,d in G.edges.data() if d['p'] < alpha)