我们从Python开源项目中，提取了以下13个代码示例，用于说明如何使用networkx.density()。
def manage_data(domain_name): 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) ass = nx.degree_assortativity_coefficient(DG) nodes_count = len(node_cname)+len(node_ip)+len(node_main) print nodes_count edges_count = len(edges) average_degree = sum(nx.degree(DG).values()) print domain_name,ass print nx.density(DG) return nodes_count,edges_count, ass,average_degree,nx.degree_histogram(DG)
def get_data_prop(self): prop = super(frontendNetwork, self).get_data_prop() if self.is_symmetric(): nnz = np.triu(self.data).sum() else: nnz = self.data.sum() _nnz = self.data.sum(axis=1) d = {'instances': self.data.shape[1], 'nnz': nnz, 'nnz_mean': _nnz.mean(), 'nnz_var': _nnz.var(), 'density': self.density(), 'diameter': self.diameter(), 'clustering_coef': self.clustering_coefficient(), 'modularity': self.modularity(), 'communities': self.clusters_len(), 'features': self.get_nfeat(), 'directed': not self.is_symmetric() } prop.update(d) return prop
def template(self, d): d['time'] = d.get('time', None) netw_templ = '''###### $corpus Building: $time minutes Nodes: $instances Links: $nnz Degree mean: $nnz_mean Degree var: $nnz_var Diameter: $diameter Modularity: $modularity Clustering Coefficient: $clustering_coef Density: $density Communities: $communities Relations: $features Directed: $directed \n''' return super(frontendNetwork, self).template(d, netw_templ)
def info_list(graph): """Returns useful information about the graph as a list of tuples :param pybel.BELGraph graph: A BEL graph :rtype: list """ number_nodes = graph.number_of_nodes() result = [ ('Nodes', number_nodes), ('Edges', graph.number_of_edges()), ('Citations', count_unique_citations(graph)), ('Authors', count_unique_authors(graph)), ('Network density', nx.density(graph)), ('Components', nx.number_weakly_connected_components(graph)), ] try: result.append(('Average degree', sum(graph.in_degree().values()) / float(number_nodes))) except ZeroDivisionError: log.info('Graph has no nodes') if graph.warnings: result.append(('Compilation warnings', len(graph.warnings))) return result
def calculate_density(graph): print "\n\tCalculating density..." g = graph dens = nx.density(g) print "\t > Graph density:", dens return g, dens
def density(Network): density = [] density.append(round(nx.density(G))) # adjacent matrix
def run(self, ips, imgs, para = None): titles = ['PartID', 'Noeds', 'Edges', 'TotalLength', 'Density', 'AveConnect'] k, unit = ips.unit gs = nx.connected_component_subgraphs(ips.data, False) if para['parts'] else [ips.data] comid, datas = 0, [] for g in gs: sl = 0 for (s, e) in g.edges(): sl += sum([i['weight'] for i in g[s][e].values()]) datas.append([comid, g.number_of_nodes(), g.number_of_edges(), round(sl*k, 2), round(nx.density(g), 2), round(nx.average_node_connectivity(g),2)][1-para['parts']:]) comid += 1 print(titles, datas) IPy.table(ips.title+'-graph', datas, titles[1-para['parts']:])
def density(self): g = self.getG() return nx.density(g)
def graphDensity(self): self.G.clear() with open(self.filename, 'r') as f: for line in f.readlines(): transaction = line.strip().split(self.delimeter) if len(transaction) == 1: self.G.add_node(transaction[0]) elif len(transaction) > 1: for i in range(len(transaction) - 1): for j in range(i + 1, len(transaction)): self.G.add_edges_from([(transaction[i], transaction[j])]) else: return -1 # empty transaction found return nx.density(self.G)
