def analyze_small_world():
# Small-world
sigma = nx.sigma(G)
omega = nx.omega(G)
table = prettytable.PrettyTable(['sigma', 'omega'])
table.add_row([sigma, omega])
print(table)
def small_world_sigma(self,graph):
"""
Returns the small-world coefficient (sigma) of the given graph. sigma = C/Cr / L/Lr
where:
C = the average clustering coefficient
L = the average shortest path length of G
Cr = the average clustering coefficient
Lr = average shortest path length of an equivalent random graph.
"""
return nx.sigma(graph)
def test_sigma():
Gs = nx.connected_watts_strogatz_graph(50, 6, 0.1, seed=rng)
Gr = nx.connected_watts_strogatz_graph(50, 6, 1, seed=rng)
sigmas = sigma(Gs, niter=1, nrand=2, seed=rng)
sigmar = sigma(Gr, niter=1, nrand=2, seed=rng)
assert_true(sigmar < sigmas)
def test_sigma():
Gs = nx.connected_watts_strogatz_graph(100, 6, 0.1)
Gr = nx.connected_watts_strogatz_graph(100, 6, 1)
sigmas = sigma(Gs, niter=1, nrand=2)
sigmar = sigma(Gr, niter=1, nrand=2)
assert_true(sigmar < sigmas)
import pandas as pd
import networkx as nx
actors_edges = pd.read_csv('actor_edge.csv')
# actors_nodes = pd.read_csv('actor_nodes.csv')
#init Graph
G = nx.Graph()
G = nx.from_pandas_edgelist(actors_edges, "Source", "Target")
#betweenness centrality
bet_centrality = nx.betweenness_centrality(G, k=1000)
#small-world
sigma = nx.sigma(G)
print("Small world sigma is " + sigma)
g_dic['Clustering_W'].append(nx.average_clustering(temp_g, weight = 'STTC_rec'))
if nx.is_connected(temp_g) == True:
g_dic['Char Path'].append(nx.average_shortest_path_length(temp_g))
g_dic['Char Path_w'].append(nx.average_shortest_path_length(temp_g, weight = 'STTC_rec'))
g_dic['Global Eff'].append(nx.global_efficiency(temp_g))
g_dic['Major C'].append(nx.number_of_nodes(temp_g)/nx.number_of_nodes(temp_g)*100)
g_dic['Sigma'].append(nx.(temp_g))
g_dic['Omega'].append(nx.omega(temp_g))
else:
print(ii, 'is not connected')
Gc = max(nx.connected_component_subgraphs(temp_g), key=len)
g_dic['Char Path'].append(nx.average_shortest_path_length(Gc))
g_dic['Char Path_w'].append(nx.average_shortest_path_length(Gc, weight = 'STTC_rec'))
g_dic['Global Eff'].append(nx.global_efficiency(Gc))
g_dic['Major C'].append(nx.number_of_nodes(Gc)/nx.number_of_nodes(temp_g)*100)
g_dic['Sigma'].append(nx.sigma(Gc))
g_dic['Omega'].append(nx.omega(Gc))
g_df = pd.DataFrame(g_dic)
g_df = g_df.drop(24) # outlier, only 10 nodes
# PROPORTION OF CONNECTED AND FRAGMENTED GRPAHS
wt_con = g_df['Gen_type'][(g_df['Major C']== 100) & (g_df['Gen_type'] == 'WT')].count()
wt_disc = g_df['Gen_type'][(g_df['Major C']< 100) & (g_df['Gen_type'] == 'WT')].count()
ko_con = g_df['Gen_type'][(g_df['Major C']== 100) & (g_df['Gen_type'] == 'KO')].count()
ko_disc = g_df['Gen_type'][(g_df['Major C']< 100) & (g_df['Gen_type'] == 'KO')].count()
cont_tab = np.array([[wt_con, wt_disc], [ko_con, ko_disc]])
sp.stats.fisher_exact(cont_tab)
disconnected_Nxs = (g_df['Gen_type'][(g_df['Major C']< 100) & (g_df['Gen_type'] == 'WT')].count()/g_df['Gen_type'][g_df['Gen_type'] == 'WT'].count()).round(2), (g_df['Gen_type'][(g_df['Major C']< 100) & (g_df['Gen_type'] == 'KO')].count()/g_df['Gen_type'][g_df['Gen_type'] == 'KO'].count()).round(2)