def test_windmill_graph(): for n in range(2, 20, 3): for k in range(2, 20, 3): G = nx.windmill_graph(n, k) assert G.number_of_nodes() == (k - 1) * n + 1 assert G.number_of_edges() == n * k * (k - 1) / 2 assert G.degree(0) == G.number_of_nodes() - 1 for i in range(1, G.number_of_nodes()): assert G.degree(i) == k - 1 pytest.raises(nx.NetworkXError, nx.ring_of_cliques, 1, 3) pytest.raises(nx.NetworkXError, nx.ring_of_cliques, 15, 0)
def test_windmill_graph(): for n in range(2,20): for k in range(2,20): G = nx.windmill_graph(n,k) assert_equal(G.number_of_nodes(), (k-1)*n+1) assert_equal(G.number_of_edges(), n*k*(k-1)/2) assert_equal(G.degree(0), G.number_of_nodes()-1) for i in range(1, G.number_of_nodes()): assert_equal(G.degree(i), k-1) assert_raises(nx.NetworkXError, nx.ring_of_cliques, 1, 3) assert_raises(nx.NetworkXError, nx.ring_of_cliques, 15, 0)
def test_windmill_graph(): for n in range(2, 20, 3): for k in range(2, 20, 3): G = nx.windmill_graph(n, k) assert_equal(G.number_of_nodes(), (k - 1) * n + 1) assert_equal(G.number_of_edges(), n * k * (k - 1) / 2) assert_equal(G.degree(0), G.number_of_nodes() - 1) for i in range(1, G.number_of_nodes()): assert_equal(G.degree(i), k - 1) assert_raises(nx.NetworkXError, nx.ring_of_cliques, 1, 3) assert_raises(nx.NetworkXError, nx.ring_of_cliques, 15, 0)
"% infected at the end: {0:.2f}%".format(infected_percentage)) plt.show() Simulate(G, plot_all=True, plot_final=True) Simulate(G, plot_all=True, plot_final=True) G1 = nx.relaxed_caveman_graph(10, 6, 0.3, seed=17) Populate(G1) infection_seed = np.random.randint(len(G1.nodes())) G1.nodes[infection_seed]['I'] = 'red' nx.draw_networkx(G1, node_color=Color(G1), with_labels=False, node_size=30) G2 = nx.windmill_graph(4, 5) Populate(G2) infection_seed = np.random.randint(len(G2.nodes())) G2.nodes[infection_seed]['I'] = 'red' nx.draw_networkx(G2, node_color=Color(G2), with_labels=False, node_size=30) new_infections = [] infected_history = [1] Simulate(G1, plot_all=True) new_infections = [] infected_history = [1]
i=int(input("enter i value")) #To choose type of the graph if i == 1: G = nx.cycle_graph(n)#creates cycle graph of n nodes print(nx.info(G)) A = nx.adjacency_matrix(G) sp.set_printoptions(linewidth=sp.inf) sp.set_printoptions(threshold=sys.maxsize) print(A.todense(),file=open("cycle_graph\%d.txt"%(n),'w')) #Outputs the adjacency matrix to a textfile and stores it in a folder called 'cycle graph' nx.draw(G) plt.show() elif i == 2: G = nx.wheel_graph(n) #creates wheel graph of n nodes print(nx.info(G)) A = nx.adjacency_matrix(G) #creates an adjacency matrix for graph G sp.set_printoptions(linewidth=sp.inf) sp.set_printoptions(threshold=sys.maxsize) print(A.todense(),file=open("wheel_graph\%d.txt"%(n),'w')) #Outputs the adjacency matrix to a textfile and stores it in a folder called 'wheel graph' nx.draw(G) plt.show() elif i == 3: k=int(input("enter k value")) %Clique size G = nx.windmill_graph(n,k) #creates wheel graph of n nodes print(nx.info(G)) A = nx.adjacency_matrix(G) sp.set_printoptions(linewidth=sp.inf) sp.set_printoptions(threshold=sys.maxsize) print(A.todense(),file=open("windmill_graph\%d_%d.txt"%(n,k),'w')) #Outputs the adjacency matrix to a textfile and stores it in a folder called 'wheel graph' nx.draw(G) plt.show()
# coding: utf-8 """ @author Liuchen 2019 """ import networkx as nx import utils import dgcnn_models as dgcnn # G = nx.generators.erdos_renyi_graph(300, 0.1) # nx 生成ER随机网络 G = nx.windmill_graph(100, 5) # nx生成的一种特殊结构的网络 # 使用说明: # 把数据构造出一个networkx 的Graphs,替换这里的G即可。若每个节点有属性向量,可作为各节点的 “feature” 属性, # 然后在create_dataset方法中将has_feature设为True即可。 # 例: G.nodes[0]['feature'] = [0., 1., 0., 1.] # 为G中的节点0添加了"feature"属性,值为[0., 1., 0., 1.] # 注意:要么所有节点都没有feature,要么全都有且长度一致 train_set, val_set, test_set = utils.create_dataset(G, val_num=100, test_num=100, hop=1, max_hop_nodes=None, link_percent=1.0, has_feature=False, embedding_dim=64, inject_neg_links=True, multi_process=True) class_num = len(train_set[0].label) # 类别数量