python类out_degree_centrality()的实例源码

def central_list(E):
    centralities = []
    centralities.append(nx.in_degree_centrality(E))
    centralities.append(nx.out_degree_centrality(E))
    centralities.append(nx.closeness_centrality(E))
    centralities.append(nx.betweenness_centrality(E))
    centralities.append(nx.eigenvector_centrality(E))

    for node in E.nodes_iter():
      measures = ("\t").join(map(lambda f: str(f[node]), centralities))
      print("%s: %s" % (node, measures))

def sorting(E):
    in_degree_central = nx.in_degree_centrality(E)
    sorted(in_degree_central.items(), key=lambda x: x[1], reverse=True)[:10] 

    out_degree_central = nx.out_degree_centrality(E)
    sorted(out_degree_central.items(), key=lambda x: x[1], reverse=True)[:10]
    print(in_degree_central)
    print(out_degree_central)

def centrality(DG):
    in_degree_centrality = nx.in_degree_centrality(DG)
    out_degree_centrality = nx.out_degree_centrality(DG)
    with open('/home/sun/PycharmProjects/Network/in_degree_centrality.csv', 'w') as f:
        for k, v in in_degree_centrality.items():
            f.write(str(k) + ': ' + str(v) + '\n')
        f.close()

    with open('/home/sun/PycharmProjects/Network/out_degree_centrality.csv', 'w') as f:
        for k, v in out_degree_centrality.items():
            f.write(str(k) + ': ' + str(v) + '\n')
        f.close()


# def main():
#     data = '/home/sun/PycharmProjects/Network/C-elegans-frontal.txt'
#     # data = 'www.adj'
#     DG = create_network(data)
#
#     # draw_network(DG)
#     # clustering_coefficient(DG)
#     # centrality(DG)
#     degree_distribution(DG)
#
# if __name__ == '__main__':
#     main()
#
#     # DG = nx.DiGraph()
#     # DG.add_edge(1,2)
#     # print(DG.edges())
#     # # pos = nx.nx_agraph.graphviz_layout(DG)
#     # nx.draw_networkx(DG, pos = nx.spring_layout(DG))
#     # plt.show()
#     # plt.ishold()
#     # plt.draw(DG)

def out_degree_centrality(self):
        """
        Parameters
        ----------

        Returns
        -------
        NxGraph: Graph object

        Examples
        --------
        >>>
        """
        return nx.out_degree_centrality(self._graph)

def graph_info(g):
    result = {}
    components = list(nx.strongly_connected_component_subgraphs(g))
    in_degrees = g.in_degree()
    out_degrees = g.out_degree()
    highest_in_degree_node = sorted(in_degrees, key = lambda x: in_degrees[x], reverse = True)[0]
    highest_out_degree_node = sorted(out_degrees, key = lambda x: out_degrees[x], reverse = True)[0]

    result['highest in_degree node'] = highest_in_degree_node
    result['highest out_degree_node'] = highest_out_degree_node

    result['numnber of components'] = len(components)
    result['number of nodes'] = g.number_of_nodes()
    result['number of edges'] = g.number_of_edges()
#Degree centrality
    in_degree_centrality = nx.in_degree_centrality(g)
    out_degree_centrality = nx.out_degree_centrality(g)
    result['sorted in_degree centrality'] = sorted([(el,in_degree_centrality[el]) for el in g.nodes()], key = lambda x: x[1], reverse = True)
    result['sorted out_degree centrality'] = sorted([(el,out_degree_centrality[el]) for el in g.nodes()], key = lambda x: x[1], reverse = True)

    result['closeness_centrality'] = sorted([(el,nx.closeness_centrality(g)[el]) for el in nx.closeness_centrality(g)], key = lambda x: x[1], reverse = True)
    result['highest in_degree node closeness'] = nx.closeness_centrality(g)[highest_in_degree_node]
    result['highest out_degree node closeness'] = nx.closeness_centrality(g)[highest_out_degree_node]


    result['betweenness centrality'] = sorted([(el,nx.betweenness_centrality(g)[el]) for el in nx.betweenness_centrality(g)], key = lambda x: x[1], reverse = True)
    result['highest in_degree node betweenness'] = nx.betweenness_centrality(g)[highest_in_degree_node]
    result['highest in_degree node betweenness'] = nx.betweenness_centrality(g)[highest_out_degree_node]


    largest_component = sorted (components, key = lambda x: x.number_of_nodes(), reverse = True)[0]

    result['largest strongly component percent'] = largest_component.number_of_nodes()/float(g.number_of_nodes())
    result['largest strongly component diameter'] = nx.diameter(largest_component)
    result['largest strongly component average path length'] = nx.average_shortest_path_length(largest_component)
    result['average_degree (undireceted)'] = sum(g.degree().values())/float(g.number_of_nodes())
    result['avg_cluster_coefficient (transitivity)'] = nx.transitivity(g)
    return result

def main():
    domain_name = 'baidu.com'
    domain_pkts = get_data(domain_name)
    node_cname, node_ip, visit_total, edges, node_main = get_ip_cname(domain_pkts[0]['details'])
    for i in domain_pkts[0]['details']:
        for v in i['answers']:
            edges.append((v['domain_name'],v['dm_data']))

    DG = nx.DiGraph()
    DG.add_edges_from(edges)

    # ?????????IP?node
    for node in DG:
        if node in node_main and DG.successors(node) in node_ip:
            print node

    # ??cname???IP????
    for node in DG:
        if node in node_cname and DG.successors(node) not in node_cname:  # ???ip?????cname
            print "node",DG.out_degree(node),DG.in_degree(node),DG.degree(node)
    # ?cname???????
    # for node in DG:
    #     if node in node_cname and DG.predecessors(node) not in node_cname:
    #         print len(DG.predecessors(node))

    for node in DG:
        if node in  node_main:
            if len(DG.successors(node)) ==3:
                print node
                print DG.successors(node)
    # print sorted(nx.degree(DG).values())

    print nx.degree_assortativity_coefficient(DG)
    average_degree = sum(nx.degree(DG).values())/(len(node_cname)+len(node_ip)+len(node_main))
    print average_degree
    print len(node_cname)+len(node_ip)+len(node_main)
    print len(edges)
    print nx.degree_histogram(DG)
    # print nx.degree_centrality(DG)
    # print nx.in_degree_centrality(DG)
    # print nx.out_degree_centrality(DG)
    # print nx.closeness_centrality(DG)
    # print nx.load_centrality(DG)