python类eigenvector_centrality()的实例源码

def CentralityMeasures(G):
    # Betweenness centrality
    bet_cen = nx.betweenness_centrality(G)
    # Closeness centrality
    clo_cen = nx.closeness_centrality(G)
    # Eigenvector centrality
    eig_cen = nx.eigenvector_centrality(G)
    # Degree centrality
    deg_cen = nx.degree_centrality(G)
    #print bet_cen, clo_cen, eig_cen
    print "# Betweenness centrality:" + str(bet_cen)
    print "# Closeness centrality:" + str(clo_cen)
    print "# Eigenvector centrality:" + str(eig_cen)
    print "# Degree centrality:" + str(deg_cen)


#main function

def CentralityMeasures(G):
    # Betweenness centrality
    bet_cen = nx.betweenness_centrality(G)
    # Closeness centrality
    clo_cen = nx.closeness_centrality(G)
    # Eigenvector centrality
    eig_cen = nx.eigenvector_centrality(G)
    # Degree centrality
    deg_cen = nx.degree_centrality(G)
    #print bet_cen, clo_cen, eig_cen
    print "# Betweenness centrality:" + str(bet_cen)
    print "# Closeness centrality:" + str(clo_cen)
    print "# Eigenvector centrality:" + str(eig_cen)
    print "# Degree centrality:" + str(deg_cen)


#main function

def CentralityMeasures(G):
    # Betweenness centrality
    bet_cen = nx.betweenness_centrality(G)
    # Closeness centrality
    clo_cen = nx.closeness_centrality(G)
    # Eigenvector centrality
    eig_cen = nx.eigenvector_centrality(G)
    # Degree centrality
    deg_cen = nx.degree_centrality(G)
    #print bet_cen, clo_cen, eig_cen
    print "# Betweenness centrality:" + str(bet_cen)
    print "# Closeness centrality:" + str(clo_cen)
    print "# Eigenvector centrality:" + str(eig_cen)
    print "# Degree centrality:" + str(deg_cen)


#main function

def get_user_to_eigenvector_centrality(self, G):
        return nx.eigenvector_centrality(G)

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 Eigen_Centrality(G):
    Eigen_Centrality = nx.eigenvector_centrality(G)
    #print "Eigen_Centrality:", sorted(Eigen_Centrality.iteritems(), key=lambda d:d[1], reverse = True)
    return Eigen_Centrality


#*****************************************************************************

def Eigen_Centrality(G):
    Eigen_Centrality = nx.eigenvector_centrality(G)
    #print "Eigen_Centrality:", sorted(Eigen_Centrality.iteritems(), key=lambda d:d[1], reverse = True)
    return Eigen_Centrality


#**********************************************************************************

def eigenvector(self):
        """ Compute the eigenvector centrality for the graph G.

        Returns
        -------
        nodes : dictionary
            Dictionary of nodes with eigenvector centrality as the value.

        Examples
        --------
        >>>
        """
        return nx.eigenvector_centrality(self._graph, weight=self._weight_field)

    # @property

def draw_network_value(orig_g, mG):
    """
    Network values: The distribution of eigenvector components (indicators of "network value")
    associated to the largest eigenvalue of the graph adjacency matrix has also been found to be
    skewed (Chakrabarti et al., 2004).
    """
    eig_cents = [nx.eigenvector_centrality_numpy(g) for g in mG]  # nodes with eigencentrality

    srt_eig_cents = sorted(eig_cents, reverse=True)
    net_vals = []
    for cntr in eig_cents:
        net_vals.append(sorted(cntr.values(), reverse=True))
    df = pd.DataFrame(net_vals)

    plt.xscale('log')
    plt.yscale('log')
    plt.fill_between(df.columns, df.mean() - df.sem(), df.mean() + df.sem(), color='blue', alpha=0.2, label="se")

    h, = plt.plot(df.mean(), color='blue', aa=True, linewidth=4, ls='--', label="H*")
    orig, = plt.plot(sorted(nx.eigenvector_centrality(orig_g).values(), reverse=True), color='black', linewidth=4,
                     ls='-', label="H")

    plt.title('Principle Eigenvector Distribution')
    plt.ylabel('Principle Eigenvector')
    plt.tick_params(
        axis='x',  # changes apply to the x-axis
        which='both',  # both major and minor ticks are affected
        bottom='off',  # ticks along the bottom edge are off
        top='off',  # ticks along the top edge are off
        labelbottom='off')  # labels along the bottom edge are off

    plt.legend([orig, h], ['$H$', 'HRG $H^*$'], loc=3)
    # fig = plt.gcf()
    # fig.set_size_inches(5, 4, forward=True)
    plt.show()