我们从Python开源项目中，提取了以下9个代码示例，用于说明如何使用networkx.eigenvector_centrality()。
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()
