Python networkx.load_centrality函数代码示例

def load_component(seed_num, graph_json_filename=None, graph_json_str=None):
  if graph_json_filename is None and graph_json_str is None:
    return []

  G = None
  if graph_json_str is None:
    G = util.load_graph(graph_json_filename=graph_json_filename)
  else:
    G = util.load_graph(graph_json_str=graph_json_str)

  components = list(nx.connected_components(G))
  components = filter(lambda x: len(x) > 0.1 * len(G), components)
  total_size = sum(map(lambda x: len(x), components))
  total_nodes = 0
  rtn = []
  for comp in components[1:]:
    num_nodes = int(float(len(comp)) / total_size * seed_num)
    component = G.subgraph(list(comp))
    clse_cent = nx.load_centrality(component)
    collector = collections.Counter(clse_cent)
    clse_cent = collector.most_common(num_nodes)
    rtn += map(lambda (x, y): x, clse_cent)
    total_nodes += num_nodes

  num_nodes = seed_num - total_nodes
  component = G.subgraph(list(components[0]))
  clse_cent = nx.load_centrality(component)
  collector = collections.Counter(clse_cent)
  clse_cent = collector.most_common(num_nodes)
  rtn += map(lambda (x, y): x, clse_cent)
  return rtn

 def test_not_strongly_connected(self):
     b = nx.load_centrality(self.D)
     result = {0: 5./12,
               1: 1./4,
               2: 1./12,
               3: 1./4,
               4: 0.000}
     for n in sorted(self.D):
         assert_almost_equal(result[n], b[n], places=3)
         assert_almost_equal(result[n], nx.load_centrality(self.D, n), places=3)

 def test_p3_load(self):
     G=self.P3
     c=nx.load_centrality(G)
     d={0: 0.000,
        1: 1.000,
        2: 0.000}
     for n in sorted(G):
         assert_almost_equal(c[n],d[n],places=3)
     c=nx.load_centrality(G,v=1)
     assert_almost_equal(c,1.0)
     c=nx.load_centrality(G,v=1,normalized=True)
     assert_almost_equal(c,1.0)

        def load_centrality_month_airports(data):    
            df = data.copy()
            df['DateOfDeparture'] = pd.to_datetime(df['DateOfDeparture'])
            df['month'] = df['DateOfDeparture'].dt.week.astype(str)
            df['year'] = df['DateOfDeparture'].dt.year.astype(str)
            df['year_month'] = df[['month','year']].apply(lambda x: '-'.join(x),axis=1)
            df['year_month_dep'] = df[['Departure','month','year']].apply(lambda x: '-'.join(x),axis=1)
            df['year_month_arr'] = df[['Arrival','month','year']].apply(lambda x: '-'.join(x),axis=1)
            year_month = pd.unique(df['year_month'])
            G = nx.Graph()
            load_centrality = {}

            for i, item in enumerate(year_month):
                sub_df = df[df['year_month'] == item][['Departure','Arrival']]
                list_dep_arr = zip(sub_df['Departure'], sub_df['Arrival'])
                G.add_edges_from(list_dep_arr)
                #G.number_of_nodes()
                #G.number_of_edges()
                centrality_month = nx.load_centrality(G)
                centrality_month = pd.DataFrame(centrality_month.items())
                centrality_month['year_month'] = [item] * centrality_month.shape[0]
                centrality_month['airport_year_month'] = centrality_month[centrality_month.columns[[0,2]]].apply(lambda x: '-'.join(x),axis=1)
                centrality_month =dict(zip(centrality_month['airport_year_month'], centrality_month[1]))

                z = load_centrality.copy()
                z.update(centrality_month)
                load_centrality = z
            df['load_centrality_month_dep'] = df['year_month_dep'].map(load_centrality)
            df['load_centrality_month_arr'] = df['year_month_arr'].map(load_centrality)
            return df

def node_load_centrality(X):
    """
    based on networkx function: load_centrality
    """
    XX = np.zeros((X.shape[0], np.sqrt(X.shape[1])))
    for i, value in enumerate(X):
        adj_mat = value.reshape((np.sqrt(len(value)),-1))
        adj_mat = (adj_mat - np.min(adj_mat)) / (np.max(adj_mat) - np.min(adj_mat))
        adj_mat = 1 - adj_mat

#        th = np.mean(adj_mat) - 0.05
#        adj_mat = np.where(adj_mat < th, adj_mat, 0.)

        percent, th, adj_mat, triu = percentage_removed(adj_mat, 0.86)
        print("percent = {0}, threshold position = {1}, threshold = {2}\n".format(percent, th, triu[th]))

        g = nx.from_numpy_matrix(adj_mat)
        print "Graph Nodes = {0}, Graph Edges = {1} ".format(g.number_of_nodes(), g.number_of_edges())
        print "\nEdge kept ratio, {0}".format(float(g.number_of_edges())/((g.number_of_nodes()*(g.number_of_nodes()-1))/2))

        deg_cent = nx.load_centrality(g, weight = 'weight')
        node_cent = np.zeros(g.number_of_nodes())

        for k in deg_cent:
            node_cent[k] = deg_cent[k]
        XX[i] = node_cent
        print "graph {0} => mean {1}, min {2}, max {3}".format(i, np.mean(XX[i]), np.min(XX[i]), np.max(XX[i]))
#    XX = XX*100
    ss = StandardScaler()
    XX = ss.fit_transform(XX.T).T

    return XX

 def most_central(self,F=1,cent_type='betweenness'):
     if cent_type == 'betweenness':
         ranking = nx.betweenness_centrality(self.G).items()
     elif cent_type == 'closeness':
         ranking = nx.closeness_centrality(self.G).items()
     elif cent_type == 'eigenvector':
         ranking = nx.eigenvector_centrality(self.G).items()
     elif cent_type == 'harmonic':
         ranking = nx.harmonic_centrality(self.G).items()
     elif cent_type == 'katz':
         ranking = nx.katz_centrality(self.G).items()
     elif cent_type == 'load':
         ranking = nx.load_centrality(self.G).items()
     elif cent_type == 'degree':
         ranking = nx.degree_centrality(self.G).items()
     ranks = [r for n,r in ranking]
     cent_dict = dict([(self.lab[n],r) for n,r in ranking])
     m_centrality = sum(ranks)
     if len(ranks) > 0:
         m_centrality = m_centrality/len(ranks)
     #Create a graph with the nodes above the cutoff centrality- remove the low centrality nodes
     thresh = F*m_centrality
     lab = {}
     for k in self.lab:
         lab[k] = self.lab[k]
     g = Graph(self.adj.copy(),self.char_list)
     for n,r in ranking:
         if r < thresh:
             g.G.remove_node(n)
             del g.lab[n]
     return (cent_dict,thresh,g)

def load_neighbors(seed_num, graph=None, graph_json_filename=None, graph_json_str=None):
  if graph_json_filename is None and graph_json_str is None and graph is None:
    return []

  G = None
  if graph is not None:
    G = graph
  elif graph_json_str is None:
    G = util.load_graph(graph_json_filename=graph_json_filename)
  else:
    G = util.load_graph(graph_json_str=graph_json_str)

  clse_cent = nx.get_node_attributes(G, "centrality")
  if len(clse_cent) == 0:
    clse_cent = nx.load_centrality(G)
    nx.set_node_attributes(G, "centrality", clse_cent)
    print "hi load neighbors"
  
  collector = collections.Counter(clse_cent)
  clse_cent = collector.most_common(SURROUND_TOP)
  nodes = map(lambda (x, y): x, clse_cent)

  current_seed = 0
  rtn = []
  while current_seed < seed_num:
    current_node = nodes[current_seed % len(nodes)]
    current_neighbors = G.neighbors(current_node)
    rtn += random.sample(set(current_neighbors) - set(rtn) - set(nodes), 1)
    current_seed += 1

  return rtn

def augmentNodes(g):
    r1 = nx.eigenvector_centrality_numpy(g)
    r2 = nx.degree_centrality(g) # DP MY
    r3 = nx.betweenness_centrality(g)
    r5 = nx.load_centrality(g,weight='weight') # DY, WY-writename # Scientific collaboration networks: II. Shortest paths, weighted networks, and centrality, M. E. J. Newman, Phys. Rev. E 64, 016132 (2001).
    r6 = nx.pagerank(g, alpha=0.85, personalization=None, max_iter=100, tol=1e-08, nstart=None, weight='weight')
    
    if nx.is_directed(g) == True:
        r8 = nx.in_degree_centrality(g)
        r9 = nx.out_degree_centrality(g)
#        r10 = nx.hits(g, max_iter=100, tol=1e-08, nstart=None)
    else:
        r4 = nx.communicability_centrality(g)
        r7 = nx.clustering(g, weight='weight')
        
    for x in g.nodes():
        g.node[x]['eigenvector_centrality_numpy'] = r1[x]
        g.node[x]['degree_centrality'] = r2[x]  
        g.node[x]['betweenness_centrality'] = r3[x]
        g.node[x]['load_centrality'] = r5[x]  
        g.node[x]['pagerank'] = r6[x]

        if nx.is_directed(g) == True:
            g.node[x]['in_degree_centrality'] = r8[x]
            g.node[x]['out_degree_centrality'] = r9[x]
#            g.node[x]['hits'] = r10[x]
        else:
            g.node[x]['communicability_centrality'] = r4[x]
            g.node[x]['clustering'] = r7[x]
    return g        

def centrality(net):
    values ={}
    close = nx.closeness_centrality(net, normalized= True)
    eigen = nx.eigenvector_centrality_numpy(net)
    page = nx.pagerank(net)
    bet = nx.betweenness_centrality(net,normalized= True)
    flow_c = nx.current_flow_closeness_centrality(net,normalized= True)
    flow_b = nx.current_flow_betweenness_centrality(net,normalized= True)
    load = nx.load_centrality(net, normalized = True)
    com_c = nx.communicability_centrality(net)
    com_b = nx.communicability_betweenness_centrality(net, normalized= True)
    degree = net.degree()
    
    file3 = open("bl.csv",'w')
    for xt in [bet,load,degree,page,flow_b,com_c,com_b,eigen,close,flow_c]:#[impo,bet,flow_b,load,com_c,com_b] :
        for yt in [bet,load,degree,page,flow_b,com_c,com_b,eigen,close,flow_c]:#[impo,bet,flow_b,load,com_c,com_b] :
            corr(xt.values(),yt.values(),file3)
        print
        file3.write("\n")
    file3.close()
    #plt.plot(x,y, 'o')
    #plt.plot(x, m*x + c, 'r', label='Fitted line')
    #plt.show()
    #for key,item in close.iteritems() :
        #values[key] = [impo.get(key),bet.get(key),flow_b.get(key), load.get(key),com_c.get(key),com_b.get(key)]
        
    return values

def analyze_graph(G):    
    #centralities and node metrics
    out_degrees = G.out_degree()
    in_degrees = G.in_degree()
    betweenness = nx.betweenness_centrality(G)
    eigenvector = nx.eigenvector_centrality_numpy(G)
    closeness = nx.closeness_centrality(G)
    pagerank = nx.pagerank(G)
    avg_neighbour_degree = nx.average_neighbor_degree(G)
    redundancy = bipartite.node_redundancy(G)
    load = nx.load_centrality(G)
    hits = nx.hits(G)
    vitality = nx.closeness_vitality(G)
    
    for name in G.nodes():
        G.node[name]['out_degree'] = out_degrees[name]
        G.node[name]['in_degree'] = in_degrees[name]
        G.node[name]['betweenness'] = betweenness[name]
        G.node[name]['eigenvector'] = eigenvector[name]
        G.node[name]['closeness'] = closeness[name]
        G.node[name]['pagerank'] = pagerank[name]
        G.node[name]['avg-neigh-degree'] = avg_neighbour_degree[name]
        G.node[name]['redundancy'] = redundancy[name]
        G.node[name]['load'] = load[name]
        G.node[name]['hits'] = hits[name]
        G.node[name]['vitality'] = vitality[name]
        
    #communities
    partitions = community.best_partition(G)
    for member, c in partitions.items():
        G.node[member]['community'] = c   
    
    return G

 def test_p2_load(self):
     G=nx.path_graph(2)
     c=nx.load_centrality(G)
     d={0: 0.000,
        1: 0.000}
     for n in sorted(G):
         assert_almost_equal(c[n],d[n],places=3)

 def f36(self):
     start = 0
     s = nx.load_centrality(self.G).values()
     res = sum(s)
     stop = 0
     # self.feature_time.append(stop - start)
     return res

 def test_unnormalized_p3_load(self):
     G=self.P3
     c=nx.load_centrality(G,normalized=False)
     d={0: 0.000,
        1: 2.000,
        2: 0.000}
     for n in sorted(G):
         assert_almost_equal(c[n],d[n],places=3)

 def test_p3_load(self):
     G=self.P3
     c=nx.load_centrality(G)
     d={0: 0.000,
        1: 1.000,
        2: 0.000}
     for n in sorted(G):
         assert_almost_equal(c[n],d[n],places=3)

    def forUndirected(G):

        myList = [nx.eigenvector_centrality_numpy(G), 
                  nx.degree_centrality(G),
                  nx.betweenness_centrality(G),
                  nx.communicability_centrality(G), 
                  nx.load_centrality(G),   
                  nx.pagerank(G, alpha=0.85, personalization=None, max_iter=100, tol=1e-08, nstart=None, weight='weight'),
                  nx.clustering(G, weight='weight')]
        return myList