python类degree_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 Nodes_Ranking(G, index):
    #Katz_Centrality = nx.katz_centrality(G)
    #print "Katz_Centrality:", sorted(Katz_Centrality.iteritems(), key=lambda d:d[1], reverse = True)
    #Page_Rank(G)
    if index == "degree_centrality":
        return Degree_Centrality(G)
    if index == "degree_mass_Centrality":
        return Degree_Mass_Centrality(G)
    if index == "between_centrality":
        return Between_Centrality(G)
    if index == "closeness_centrality":
        return Closeness_Centrality(G)
    if index == "kshell_centrality":
        return KShell_Centrality(G)
    if index == "eigen_centrality":
        return Eigen_Centrality_Andy(G)
    if index == "collective_influence":
        return Collective_Influence(G)
    if index == "enhanced_collective_centrality":
        return Enhanced_Collective_Influence(G)
    if index == "hybrid_diffusion_centrality":
        return Hybrid_Diffusion_Centrality(G)

def out_degree_centrality(G):
    """Compute the out-degree centrality for nodes.

    The out-degree centrality for a node v is the fraction of nodes its
    outgoing edges are connected to.

    Parameters
    ----------
    G : graph
        A NetworkX graph

    Returns
    -------
    nodes : dictionary
        Dictionary of nodes with out-degree centrality as values.

    See Also
    --------
    degree_centrality, in_degree_centrality

    Notes
    -----
    The degree centrality values are normalized by dividing by the maximum
    possible degree in a simple graph n-1 where n is the number of nodes in G.

    For multigraphs or graphs with self loops the maximum degree might
    be higher than n-1 and values of degree centrality greater than 1
    are possible.
    """
    if not G.is_directed():
        raise nx.NetworkXError( \
            "out_degree_centrality() not defined for undirected graphs.")
    centrality = {}
    s = 1.0 / (len(G) - 1.0)
    centrality = dict((n, d * s) for n, d in G.out_degree_iter())
    return centrality

def calculate_degree_centrality(graph):
    print "\nCalculating Degree Centrality..."
    g = graph
    dc = nx.degree_centrality(g)
    nx.set_node_attributes(g, 'degree_cent', dc)
    degcent_sorted = sorted(dc.items(), key=itemgetter(1), reverse=True)
    for key, value in degcent_sorted[0:10]:
        print "   > ", key, round(value, 4)

    return graph, dc

def UpdateThresholdDegree(self):
        self.g =  self.Graph_data().DrawHighlightedGraph(self.EdgeSliderValue)

        # Degree Centrality for the the nodes involved
        self.Centrality=nx.degree_centrality(self.g)
        self.Betweeness=nx.betweenness_centrality(self.g)  
        self.ParticipationCoefficient = self.communityDetectionEngine.participation_coefficient(self.g,True)
        self.LoadCentrality = nx.load_centrality(self.g)
        self.ClosenessCentrality = nx.closeness_centrality(self.g)

        for i in range(len(self.ParticipationCoefficient)):
            if (str(float(self.ParticipationCoefficient[i])).lower() == 'nan'):
                   self.ParticipationCoefficient[i] = 0

        i = 0
        """ Calculate rank and Zscore """
        MetrixDataStructure=eval('self.'+self.nodeSizeFactor)

        from collections import OrderedDict
        self.sortedValues = OrderedDict(sorted(MetrixDataStructure.items(), key=lambda x:x[1]))

        self.average = np.average(self.sortedValues.values())
        self.std = np.std(self.sortedValues.values())

        for item in self.scene().items():
            if isinstance(item, Node):
                Size = eval('self.'+self.nodeSizeFactor+'[i]')
                rank, Zscore = self.calculateRankAndZscore(i)
                item.setNodeSize(Size,self.nodeSizeFactor,rank,Zscore)    
                i = i + 1

        self.ThresholdChange.emit(True)
        if not(self.ColorNodesBasedOnCorrelation): 
            self.Ui.communityLevelLineEdit.setText(str(self.level))
            self.DendoGramDepth.emit(self.level)

        self.Refresh()

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

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

def Nodes_Ranking(G, index):
    if index == "degree_centrality":
        return Degree_Centrality(G)
    if index == "between_centrality":
        return Between_Centrality(G)
    if index == "closeness_centrality":
        return Closeness_Centrality(G)
    if index == "pagerank_centrality":
        return Page_Rank(G)
    if index == "kshell_centrality":
        return KShell_Centrality(G)
    if index == "collective_influence":
        return Collective_Influence(G)
    if index == "enhanced_collective_centrality":
        return Enhanced_Collective_Influence(G)

    if index == "eigen_centrality":
        return Eigen_Centrality_Avg(G) #Eigen_Centrality_Andy(G)

    if index == "md_eigen_centrality":
        return MD_Eigen_Centrality_Andy(G)
    if index == "hc_eigen_centrality":
        return HC_Eigen_Centrality_Andy(G)

    #if index == "hybrid_diffusion_centrality":
    #    return Hybrid_Diffusion_Centrality(G)


    if index == "PIR_Centrality": #i.e. weighted_hybrid_diffusion_centrality
        return PIR_Centrality_Avg(G) #Weighted_Hybrid_Diffusion_Centrality(G)

def adj_to_degree(y):
    # @debug: dont' call nxG or do a native integration !

    # To convert normalized degrees to raw degrees
    #ba_c = {k:int(v*(len(ba_g)-1)) for k,v in ba_c.iteritems()}
    G = nxG(y)
    #degree = sorted(nx.degree(G).values(), reverse=True)

    #ba_c = nx.degree_centrality(G)
    return nx.degree(G)

def main(filename, type, constructed_graph = -1):
    # 1. original graph
    original_graph_path = os.path.join("data",filename,"")
    original_graph = generate_graph(original_graph_path,filename,-1)
    plt.figure("original graph degree distribution")
    draw_degree(original_graph)
    print('original edge number: ',len(original_graph.edges()))


    # 2. reconstruct graph
    if constructed_graph == -1:
        reconstruct_graph_path = os.path.join("reconstruction", filename, type,"")
        reconstruct_graph_adj = pickle.load(open(glob.glob(reconstruct_graph_path+"*.adj")[0],'rb'))
    else:
        reconstruct_graph_adj = constructed_graph
    reconstruct_graph = adj2Graph(reconstruct_graph_adj, edgesNumber = len(original_graph.edges()))
    print('edge number: ', len(reconstruct_graph.edges()))
    plt.figure("reconstruct graph degree distribution")
    draw_degree(reconstruct_graph)

    print("Clustering: ",nx.average_clustering(original_graph), ' ', nx.average_clustering(reconstruct_graph))
    # print("Diameter: ", nx.average_shortest_path_length(original_graph), ' ', nx.average_shortest_path_length(reconstruct_graph))
    # print("degree centrality: ", nx.degree_centrality(original_graph), ' ',  nx.degree_centrality(reconstruct_graph))
    #print("closeness centrality: ", nx.closeness_centrality(original_graph), ' ', nx.closeness_centrality(reconstruct_graph))

    plt.show()

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

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

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

def calculate_centrality(G):
    degc = nx.degree_centrality(G)
    nx.set_node_attributes(G,'degree_cent', degc)
    degc_sorted = sorted(degc.items(), key=valuegetter(1), reverse=True)
    for key, value in degc_sorted[0:10]:
        print "Degree Centrailty:", key, value
    return G, degc

def changeLayout(self,Layout='sfdp'):
        Layout = (Layout.encode('ascii','ignore')).replace(' ','')
        self.g =  self.Graph_data().DrawHighlightedGraph(self.EdgeSliderValue)

        # asking community detection Engine to compute the Layout
        self.pos,Factor = self.communityDetectionEngine.communityLayoutCalculation(Layout,self.g)

        # Degree Centrality for the the nodes involved
        self.Centrality=nx.degree_centrality(self.g)
        self.Betweeness=nx.betweenness_centrality(self.g)  
        self.LoadCentrality = nx.load_centrality(self.g)
        self.ParticipationCoefficient = self.communityDetectionEngine.participation_coefficient(self.g,True)
        self.ClosenessCentrality = nx.closeness_centrality(self.g)

        for i in range(len(self.ParticipationCoefficient)):
            if (str(float(self.ParticipationCoefficient[i])).lower() == 'nan'):
                   self.ParticipationCoefficient[i] = 0
        i = 0 

        """ Calculate rank and Zscore """
        MetrixDataStructure=eval('self.'+self.nodeSizeFactor)
        from collections import OrderedDict

        self.sortedValues = OrderedDict(sorted(MetrixDataStructure.items(), key=lambda x:x[1]))
        self.average = np.average(self.sortedValues.values())
        self.std = np.std(self.sortedValues.values())

        for item in self.scene().items():
            if isinstance(item, Node):
                x,y=self.pos[i]
                item.setPos(QtCore.QPointF(x,y)*Factor)
                Size = eval('self.'+self.nodeSizeFactor+'[i]')
                rank, Zscore = self.calculateRankAndZscore(i)
                item.setNodeSize(Size,self.nodeSizeFactor,rank,Zscore)
                i = i + 1

        for edge in self.edges:
            edge().adjust()

        self.Refresh()

        if not(self.PositionPreserve):
            self.Scene_to_be_updated.setSceneRect(self.Scene_to_be_updated.itemsBoundingRect())
            self.setScene(self.Scene_to_be_updated)

        self.fitInView(self.Scene_to_be_updated.itemsBoundingRect(),QtCore.Qt.KeepAspectRatio)
        self.Scene_to_be_updated.update()

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)