我们从Python开源项目中，提取了以下18个代码示例，用于说明如何使用networkx.betweenness_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 authorNet_feature(): # output: compute the author centrialy for each author # author centrality dict authorCo = pickle.load(open(cspath+"coauthor","rb")) # nodeSet = set() edgeSet = set() for key,val in authorCo.iteritems(): nodeSet.add(key) edgeSet.update([(key,item) for item in val if item!=key]) pickle.dump(nodeSet,open(cspath+"co_nodeSet","wb")) pickle.dump(edgeSet,open(cspath+"co_edgeSet","wb")) g = nx.Graph() g.add_nodes_from(nodeSet) g.add_edges_from(edgeSet) interested_node = None clo_cen = defaultdict(int) for node in g.nodes(): clo_cen[node]=1 # Closeness centrality #clo_cen = nx.betweenness_centrality(g, k=int(len(g.nodes())/5)) #centrality is time-consuming, denote this in real atmosphere pickle.dump(clo_cen,open(cspath+"author_cen","wb")) print 'authorNet_feature finish'
def calculate_betweenness(graph): print "\n\tCalculating Betweenness Centrality..." g = graph bc = nx.betweenness_centrality(g.to_undirected()) nx.set_node_attributes(g, 'betweenness', bc) degbetw_sorted = sorted(bc.items(), key=itemgetter(1), reverse=True) for key, value in degbetw_sorted[0:10]: print "\t > ", key, round(value, 4) return g, bc
def main(opts): df = pd.read_csv(opts['biogrid'], sep='\t') interact_df = df[['Official Symbol Interactor A', 'Official Symbol Interactor B']] interact_genes = interact_df.dropna().values.tolist() G = nx.Graph() G.add_edges_from(map(tuple, interact_genes)) gene_betweeness = nx.betweenness_centrality(G) gene_degree = G.degree() result = [[key, gene_betweeness[key], gene_degree[key]] for key in gene_degree] result = [['gene', 'gene_betweeness', 'gene_degree']] + result with open(opts['output'], 'wb') as handle: csv.writer(handle, delimiter='\t').writerows(result)
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 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 Between_Centrality(G): Bet_Centrality = nx.betweenness_centrality(G) #print "Bet_Centrality:", sorted(Bet_Centrality.iteritems(), key=lambda d:d[1], reverse = True) return Bet_Centrality
def top_centrality() : citations = db.select(["citing", "cited"], table="graph", limit=1000000) print len(citations) graph = nx.DiGraph() for citing, cited in progress(citations, 10000) : graph.add_edge(int(citing), int(cited)) print graph.number_of_nodes() centrality = nx.betweenness_centrality(graph) print centrality.items()[:100]
def betweenness_centrality(self): """ Parameters ---------- Returns ------- NxGraph: Graph object Examples -------- >>> """ return nx.betweenness_centrality(self._graph, weight=self._weight_field)
def __init__(self, method='degree', analyzer=NltkNormalizer().split_and_normalize): self.analyze = analyzer self.method = method self.methods_on_digraph = {'hits', 'pagerank', 'katz'} self._get_scores = {'degree': nx.degree, 'betweenness': nx.betweenness_centrality, 'pagerank': nx.pagerank_scipy, 'hits': self._hits, 'closeness': nx.closeness_centrality, 'katz': nx.katz_centrality}[method] # Add a new value when a new vocabulary item is seen self.vocabulary = defaultdict() self.vocabulary.default_factory = self.vocabulary.__len__
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 central_point_dominance(self): """ Compute central point dominance. Returns ------- cpd : float Central point dominance """ bet_cen = nx.betweenness_centrality(self.to_undirected()) bet_cen = list(bet_cen.values()) cpd = sum(max(bet_cen) - np.array(bet_cen))/(len(bet_cen)-1) return cpd
def count_top_centrality(graph, number=30): dd = nx.betweenness_centrality(graph) dc = Counter(dd) return dict(dc.most_common(number))
def calculate_betweenness_centality(graph, k=CENTRALITY_SAMPLES): """Calculates the betweenness centrality over nodes in the graph. Tries to do it with a certain number of samples, but then tries a complete approach if it fails. :param pybel.BELGraph graph: A BEL graph :param int k: The number of samples to use :rtype: collections.Counter[tuple,float] """ try: res = Counter(nx.betweenness_centrality(graph, k=k)) return res except: return Counter(nx.betweenness_centrality(graph))
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()
