def analyze_structure_for_fixed_degree_seq(n, max_degree=None):
# Not sure how to aggregate this into one graph for many different
# degree sequences
mean_degrees = []
mean_neighbor_degrees = []
# Note, this is the diameter of the largest connected component
diameters = []
components_count = []
global_clustering_coefficients = []
largest_component_sizes = []
degree_sequence = generate_random_degree_sequence(n, max_degree)
for i in range(500):
G = random_graphs.configuration_model(degree_sequence, cleaned=False)
print(i)
mean_degrees.append(graph_measures.mean_degree(G))
mean_neighbor_degrees.append(graph_measures.mean_neighbor_degree(G))
diameters.append(graph_measures.diameter(G))
components_count.append(graph_measures.connected_components_count(G))
global_clustering_coefficients.append(
graph_measures.global_clustering_coefficient(G))
largest_component_sizes.append(graph_measures.largest_component(G))
# Graph results
plt.figure(1)
plt.subplot(234)
plt.hist(diameters)
plt.title("Diameter")
plt.subplot(235)
plt.hist(components_count)
plt.title("Components Count")
plt.subplot(233)
plt.hist(global_clustering_coefficients)
plt.title("Clustering Coefficient")
plt.subplot(231)
plt.hist(mean_degrees)
plt.title("Mean Degree")
plt.subplot(232)
plt.hist(mean_neighbor_degrees)
plt.title("Mean Neighbor Degree")
plt.subplot(236)
plt.hist(largest_component_sizes)
plt.title("Largest Component Size")
plt.show()
def pull_graph_statistics(G):
statistics = {}
statistics[degree_centrality] = degree_centrality(G)
statistics[harmonic_centrality] = harmonic_centrality(G)
statistics[eigenvector_centrality] = eigenvector_centrality(G)
statistics[betweenness_centrality] = betweenness_centrality(G)
statistics[diameter] = diameter(G)
statistics[mean_degree] = mean_degree(G)
statistics[mean_neighbor_degree] = mean_neighbor_degree(G)
statistics[global_clustering_coefficient] = global_clustering_coefficient(G)
statistics[degree_assortativity] = degree_assortativity(G)
statistics[coefficient_of_variation] = coefficient_of_variation(G)
return statistics
def analyze_structural_identity_graphs(Gs, fig, constraints=None):
# Not sure how to aggregate this into one graph for many different
# degree sequences
print constraints
mean_degrees = []
mean_neighbor_degrees = []
# Note, this is the diameter of the largest connected component
diameters = []
components_count = []
global_clustering_coefficients = []
largest_component_sizes = []
coefficients_of_variations = []
low_centrals = []
high_centrals = []
for i in xrange(len(Gs)):
G = Gs[i]
#print G.number_of_edges()
print "Trial, ", i
mean_degrees.append(graph_measures.mean_degree(G))
mean_neighbor_degrees.append(graph_measures.mean_neighbor_degree(G))
diameters.append(graph_measures.diameter(G))
components_count.append(graph_measures.connected_components_count(G))
global_clustering_coefficients.append(
graph_measures.global_clustering_coefficient(G))
largest_component_sizes.append(graph_measures.largest_component(G))
coefficients_of_variations.append(
graph_measures.coefficient_of_variation(G))
centrals = graph_measures.hi_lo_centrality(
graph_measures.betweenness_centrality, G)
low_centrals.append(centrals[0])
high_centrals.append(centrals[1])
# Graph results
plt.figure(fig)
kwargs = dict(histtype='stepfilled', alpha=0.5, normed=True, bins=20)
dic = {331:"Mean Degree", 332:"Mean Neighbor Degree",\
333:"Clustering Coefficient", 334:"Diameter",\
335:"Components Count", 336:"Largest Component Size",\
337:"Coefficient of Variation", 338:"Smallest Betweenness Centrality",\
339:"Largest Betweenness Centrality"}
# plt.subplot(334)
# plt.hist(diameters, **kwargs)
# plt.title("Diameter")
# plt.subplot(335)
# plt.hist(components_count, **kwargs)
# plt.title("Components Count")
# plt.subplot(333)
# plt.hist(global_clustering_coefficients, **kwargs)
# plt.title("Clustering Coefficient")
# plt.subplot(331)
# plt.hist(mean_degrees, **kwargs)
# plt.title("Mean Degree")
# plt.subplot(332)
# plt.hist(mean_neighbor_degrees, **kwargs)
# plt.title("Mean Neighbor Degree")
# plt.subplot(336)
# plt.hist(largest_component_sizes, **kwargs)
# plt.title("Largest Component Size")
# plt.subplot(337)
# plt.hist(coefficients_of_variations, **kwargs)
# plt.title("Coefficient of Variation")
# plt.subplot(338)
# plt.hist(low_centrals, **kwargs)
# plt.title("Smallest Betweenness Centrality")
# plt.subplot(339)
# plt.hist(high_centrals, **kwargs)
# plt.title("Largest Betweenness Centrality")
# plt.tight_layout()
#plt.show()
return [mean_degrees, mean_neighbor_degrees, global_clustering_coefficients,\
diameters, components_count, largest_component_sizes,\
coefficients_of_variations, low_centrals, high_centrals], dic