def smoke_test_random_graph(self): seed = 42 G=gnp_random_graph(100,0.25,seed) G=binomial_graph(100,0.25,seed) G=erdos_renyi_graph(100,0.25,seed) G=fast_gnp_random_graph(100,0.25,seed) G=gnm_random_graph(100,20,seed) G=dense_gnm_random_graph(100,20,seed) G=watts_strogatz_graph(10,2,0.25,seed) assert_equal(len(G), 10) assert_equal(G.number_of_edges(), 10) G=connected_watts_strogatz_graph(10,2,0.1,seed) assert_equal(len(G), 10) assert_equal(G.number_of_edges(), 10) G=watts_strogatz_graph(10,4,0.25,seed) assert_equal(len(G), 10) assert_equal(G.number_of_edges(), 20) G=newman_watts_strogatz_graph(10,2,0.0,seed) assert_equal(len(G), 10) assert_equal(G.number_of_edges(), 10) G=newman_watts_strogatz_graph(10,4,0.25,seed) assert_equal(len(G), 10) assert_true(G.number_of_edges() >= 20) G=barabasi_albert_graph(100,1,seed) G=barabasi_albert_graph(100,3,seed) assert_equal(G.number_of_edges(),(97*3)) G = extended_barabasi_albert_graph(100, 1, 0, 0, seed) assert_equal(G.number_of_edges(), 99) G = extended_barabasi_albert_graph(100, 3, 0, 0, seed) assert_equal(G.number_of_edges(), 97 * 3) G = extended_barabasi_albert_graph(100, 1, 0, 0.5, seed) assert_equal(G.number_of_edges(), 99) G = extended_barabasi_albert_graph(100, 2, 0.5, 0, seed) assert_greater(G.number_of_edges(), 100 * 3) assert_less(G.number_of_edges(), 100 * 4) G=extended_barabasi_albert_graph(100, 2, 0.3, 0.3, seed) assert_greater(G.number_of_edges(), 100 * 2) assert_less(G.number_of_edges(), 100 * 4) G=powerlaw_cluster_graph(100,1,1.0,seed) G=powerlaw_cluster_graph(100,3,0.0,seed) assert_equal(G.number_of_edges(),(97*3)) G=random_regular_graph(10,20,seed) assert_raises(NetworkXError, random_regular_graph, 3, 21) constructor=[(10,20,0.8),(20,40,0.8)] G=random_shell_graph(constructor,seed) G=random_lobster(10,0.1,0.5,seed)
def smoke_test_random_graph(self): seed = 42 G=gnp_random_graph(100,0.25,seed) G=binomial_graph(100,0.25,seed) G=erdos_renyi_graph(100,0.25,seed) G=fast_gnp_random_graph(100,0.25,seed) G=gnm_random_graph(100,20,seed) G=dense_gnm_random_graph(100,20,seed) G=watts_strogatz_graph(10,2,0.25,seed) assert_equal(len(G), 10) assert_equal(G.number_of_edges(), 10) G=connected_watts_strogatz_graph(10,2,0.1,seed) assert_equal(len(G), 10) assert_equal(G.number_of_edges(), 10) G=watts_strogatz_graph(10,4,0.25,seed) assert_equal(len(G), 10) assert_equal(G.number_of_edges(), 20) G=newman_watts_strogatz_graph(10,2,0.0,seed) assert_equal(len(G), 10) assert_equal(G.number_of_edges(), 10) G=newman_watts_strogatz_graph(10,4,0.25,seed) assert_equal(len(G), 10) assert_true(G.number_of_edges() >= 20) G=barabasi_albert_graph(100,1,seed) G=barabasi_albert_graph(100,3,seed) assert_equal(G.number_of_edges(),(97*3)) G = extended_barabasi_albert_graph(100, 1, 0, 0, seed) assert_equal(G.number_of_edges(), 99) G = extended_barabasi_albert_graph(100, 3, 0, 0, seed) assert_equal(G.number_of_edges(), 97 * 3) G = extended_barabasi_albert_graph(100, 1, 0, 0.5, seed) assert_equal(G.number_of_edges(), 99) G = extended_barabasi_albert_graph(100, 2, 0.5, 0, seed) assert_greater(G.number_of_edges(), 100 * 3) assert_less(G.number_of_edges(), 100 * 4) G=extended_barabasi_albert_graph(100, 2, 0.3, 0.3, seed) assert_greater(G.number_of_edges(), 100 * 2) assert_less(G.number_of_edges(), 100 * 4) G=powerlaw_cluster_graph(100,1,1.0,seed) G=powerlaw_cluster_graph(100,3,0.0,seed) assert_equal(G.number_of_edges(),(97*3)) G=random_regular_graph(10,20,seed) assert_raises(NetworkXError, random_regular_graph, 3, 21) constructor=[(10,20,0.8),(20,40,0.8)] G=random_shell_graph(constructor,seed) G=random_lobster(10,0.1,0.5,seed)
def _createGraph(self): dlg = PropertyViewer(self.name, self.icon, Nodes=Integer(5, 1, 100, 1), Degree=Integer(2,1,100,1), TriangleDensity=Float(0.5)) nodes = [] edges = [] if dlg.exec_(): values = dlg.values() n = values['Nodes'] m = values['Degree'] p = values['TriangleDensity'] G = rnd.powerlaw_cluster_graph(m, n, p) nodes = layout.circularNodes(n, 25) for i in G.edges_iter(): edges.append(i) return nodes, edges
import networkx import os import random import numpy as np from networkx.generators.random_graphs import powerlaw_cluster_graph for size in [256, 512, 1024, 2048, 4096, 8192, 16384]: print("For size: ", size) g = powerlaw_cluster_graph(size, m=15, p=0.2) # if not os.path.exists(os.path.join('../datasets', 'syn_' + str(size))): # os.makedirs(os.path.join('../datasets', 'syn_' + str(size))) current_dir = os.path.join('../datasets', 'syn_' + str(size)) probs = [0.1, 0.01, 0.001] thresh = 0.2 nodes = sorted(g.degree, key=lambda x: x[1], reverse=True) print(nodes[:4]) with open(os.path.join(current_dir, 'seeds.txt'), 'w') as f: for node in nodes[:5]: f.write(str(node[0]) + '\n') with open(os.path.join(current_dir, 'mem.txt'), 'w') as f: for i in range(size): vec = np.random.uniform(0, 1, 100) my_features = np.where(vec < thresh) # print(my_features[0]) list_f = [str(x) for x in my_features[0]] # print(list_f)
def powerlawClusterGraph(n,m=3,p=0.3): graphsToReturn = [] for i in range(100): print RandomGraphGenerator.powerlaw_cluster_graph, n, i graphsToReturn.append([i*TIME_UNIT_IN_SECONDS, my_nx.getDictForGraph(powerlaw_cluster_graph(n,m,p))]) return graphsToReturn
def smoke_test_random_graph(self): seed = 42 G = gnp_random_graph(100, 0.25, seed) G = gnp_random_graph(100, 0.25, seed, directed=True) G = binomial_graph(100, 0.25, seed) G = erdos_renyi_graph(100, 0.25, seed) G = fast_gnp_random_graph(100, 0.25, seed) G = fast_gnp_random_graph(100, 0.25, seed, directed=True) G = gnm_random_graph(100, 20, seed) G = gnm_random_graph(100, 20, seed, directed=True) G = dense_gnm_random_graph(100, 20, seed) G = watts_strogatz_graph(10, 2, 0.25, seed) assert len(G) == 10 assert G.number_of_edges() == 10 G = connected_watts_strogatz_graph(10, 2, 0.1, tries=10, seed=seed) assert len(G) == 10 assert G.number_of_edges() == 10 pytest.raises(NetworkXError, connected_watts_strogatz_graph, \ 10, 2, 0.1, tries=0) G = watts_strogatz_graph(10, 4, 0.25, seed) assert len(G) == 10 assert G.number_of_edges() == 20 G = newman_watts_strogatz_graph(10, 2, 0.0, seed) assert len(G) == 10 assert G.number_of_edges() == 10 G = newman_watts_strogatz_graph(10, 4, 0.25, seed) assert len(G) == 10 assert G.number_of_edges() >= 20 G = barabasi_albert_graph(100, 1, seed) G = barabasi_albert_graph(100, 3, seed) assert G.number_of_edges() == (97 * 3) G = extended_barabasi_albert_graph(100, 1, 0, 0, seed) assert G.number_of_edges() == 99 G = extended_barabasi_albert_graph(100, 3, 0, 0, seed) assert G.number_of_edges() == 97 * 3 G = extended_barabasi_albert_graph(100, 1, 0, 0.5, seed) assert G.number_of_edges() == 99 G = extended_barabasi_albert_graph(100, 2, 0.5, 0, seed) assert G.number_of_edges() > 100 * 3 assert G.number_of_edges() < 100 * 4 G = extended_barabasi_albert_graph(100, 2, 0.3, 0.3, seed) assert G.number_of_edges() > 100 * 2 assert G.number_of_edges() < 100 * 4 G = powerlaw_cluster_graph(100, 1, 1.0, seed) G = powerlaw_cluster_graph(100, 3, 0.0, seed) assert G.number_of_edges() == (97 * 3) G = random_regular_graph(10, 20, seed) pytest.raises(NetworkXError, random_regular_graph, 3, 21) pytest.raises(NetworkXError, random_regular_graph, 33, 21) constructor = [(10, 20, 0.8), (20, 40, 0.8)] G = random_shell_graph(constructor, seed) G = random_lobster(10, 0.1, 0.5, seed) # difficult to find seed that requires few tries seq = random_powerlaw_tree_sequence(10, 3, seed=14, tries=1) G = random_powerlaw_tree(10, 3, seed=14, tries=1)
def test_random_graph(self): seed = 42 G = gnp_random_graph(100, 0.25, seed) G = gnp_random_graph(100, 0.25, seed, directed=True) G = binomial_graph(100, 0.25, seed) G = erdos_renyi_graph(100, 0.25, seed) G = fast_gnp_random_graph(100, 0.25, seed) G = fast_gnp_random_graph(100, 0.25, seed, directed=True) G = gnm_random_graph(100, 20, seed) G = gnm_random_graph(100, 20, seed, directed=True) G = dense_gnm_random_graph(100, 20, seed) G = watts_strogatz_graph(10, 2, 0.25, seed) assert len(G) == 10 assert G.number_of_edges() == 10 G = connected_watts_strogatz_graph(10, 2, 0.1, tries=10, seed=seed) assert len(G) == 10 assert G.number_of_edges() == 10 pytest.raises(NetworkXError, connected_watts_strogatz_graph, 10, 2, 0.1, tries=0) G = watts_strogatz_graph(10, 4, 0.25, seed) assert len(G) == 10 assert G.number_of_edges() == 20 G = newman_watts_strogatz_graph(10, 2, 0.0, seed) assert len(G) == 10 assert G.number_of_edges() == 10 G = newman_watts_strogatz_graph(10, 4, 0.25, seed) assert len(G) == 10 assert G.number_of_edges() >= 20 G = barabasi_albert_graph(100, 1, seed) G = barabasi_albert_graph(100, 3, seed) assert G.number_of_edges() == (97 * 3) G = extended_barabasi_albert_graph(100, 1, 0, 0, seed) assert G.number_of_edges() == 99 G = extended_barabasi_albert_graph(100, 3, 0, 0, seed) assert G.number_of_edges() == 97 * 3 G = extended_barabasi_albert_graph(100, 1, 0, 0.5, seed) assert G.number_of_edges() == 99 G = extended_barabasi_albert_graph(100, 2, 0.5, 0, seed) assert G.number_of_edges() > 100 * 3 assert G.number_of_edges() < 100 * 4 G = extended_barabasi_albert_graph(100, 2, 0.3, 0.3, seed) assert G.number_of_edges() > 100 * 2 assert G.number_of_edges() < 100 * 4 G = powerlaw_cluster_graph(100, 1, 1.0, seed) G = powerlaw_cluster_graph(100, 3, 0.0, seed) assert G.number_of_edges() == (97 * 3) G = random_regular_graph(10, 20, seed) pytest.raises(NetworkXError, random_regular_graph, 3, 21) pytest.raises(NetworkXError, random_regular_graph, 33, 21) constructor = [(10, 20, 0.8), (20, 40, 0.8)] G = random_shell_graph(constructor, seed) def is_caterpillar(g): """ A tree is a caterpillar iff all nodes of degree >=3 are surrounded by at most two nodes of degree two or greater. ref: http://mathworld.wolfram.com/CaterpillarGraph.html """ deg_over_3 = [n for n in g if g.degree(n) >= 3] for n in deg_over_3: nbh_deg_over_2 = [ nbh for nbh in g.neighbors(n) if g.degree(nbh) >= 2 ] if not len(nbh_deg_over_2) <= 2: return False return True def is_lobster(g): """ A tree is a lobster if it has the property that the removal of leaf nodes leaves a caterpillar graph (Gallian 2007) ref: http://mathworld.wolfram.com/LobsterGraph.html """ non_leafs = [n for n in g if g.degree(n) > 1] return is_caterpillar(g.subgraph(non_leafs)) G = random_lobster(10, 0.1, 0.5, seed) assert max([G.degree(n) for n in G.nodes()]) > 3 assert is_lobster(G) pytest.raises(NetworkXError, random_lobster, 10, 0.1, 1, seed) pytest.raises(NetworkXError, random_lobster, 10, 1, 1, seed) pytest.raises(NetworkXError, random_lobster, 10, 1, 0.5, seed) # docstring says this should be a caterpillar G = random_lobster(10, 0.1, 0.0, seed) assert is_caterpillar(G) # difficult to find seed that requires few tries seq = random_powerlaw_tree_sequence(10, 3, seed=14, tries=1) G = random_powerlaw_tree(10, 3, seed=14, tries=1)
from math import log import networkx as nx import matplotlib.pyplot as plt from networkx.generators.random_graphs import powerlaw_cluster_graph from networkx.algorithms.centrality import edge_betweenness_centrality plt.rcParams["figure.figsize"] = (15, 15) G = powerlaw_cluster_graph(30, 3, 0.05) coords = nx.spectral_layout(G) # a different layout this time rank = nx.pagerank(G) # default damping print(list(rank.items())[:3]) # normalize to [0, 1] and logscale low = min(rank.values()) high = max(rank.values()) span = high - low rank = [log((rank[v] - low) / span + 1) for v in G.nodes()] opt = { 'node_size': 800, 'width': 3, 'with_labels': True, 'font_color': 'black', 'edge_color': 'orange' } fig, ax = plt.subplots() nx.draw( G, pos=coords,