generators.py 文件源码

def sdg(num_nodes, num_edges, epsilon1, epsilon2):
    gen_graph = nx.DiGraph()
    nodes = range(num_nodes)
    gen_graph.add_nodes_from(nodes)
    i = 0
    while i < num_edges:
        # choose out node uniformly
        if random.random() < epsilon1 or len(gen_graph.edges()) < 1:
            out_node = random.choice(nodes)
        # choose out based on preferential attachment
        else:
            out_nodes = gen_graph.out_degree().keys()
            out_degree = np.array(gen_graph.out_degree().values())

            out_node = np.random.choice(out_nodes, p=out_degree/(1.0 * i))
        # choose in node uniformly
        if random.random() < epsilon2 or len(gen_graph.edges()) < 1:
            if 0 not in gen_graph.in_degree().values():
                in_node = random.choice(nodes)
        # choose in based on preferential attachment
            else:

                if len(nx.isolates(gen_graph)) > 0:
                    in_node = random.choice(nx.isolates(gen_graph))
                else:
                    in_node_degree = 1
                    while in_node_degree > 0:
                        in_node = random.choice(gen_graph.nodes())
                        in_node_degree = gen_graph.in_degree(in_node)
        else:
            in_nodes = gen_graph.in_degree().keys()
            in_degree = np.array(gen_graph.in_degree().values())
            in_node = np.random.choice(in_nodes, p=in_degree / (1.0 * i))

        if out_node != in_node and (out_node, in_node) not in gen_graph.edges():
            gen_graph.add_edge(out_node, in_node)
            i += 1

    return gen_graph