print(adjac)
print(Z)
new_graph.add_edges_from(adjac)
# Draw the resulting graph
nx.draw(new_graph, with_labels=True, font_weight='bold')
S = [6, 7, 13]
T = [4, 15, 8, 14, 3, 11, 1, 10, 0]
# Conductance
sumOfCond = []
for i in range(len(cluster)):
sumOfCond.append(conductance(new_graph, cluster[i]))
condScoreS = conductance(new_graph, S)
condScoreT = conductance(new_graph, T)
overallCond = min(sumOfCond)
# Modularity Communities
barbMod = list(greedy_modularity_communities(new_graph))
# Modularity Score
barbModScore = qu.modularity(new_graph, barbMod)
# edge_betweenness_centrality
barbedgeBetweenness = nx.edge_betweenness_centrality(new_graph, None, False)
barbaverageEdge = sum(barbedgeBetweenness.values()) / len(barbedgeBetweenness)
barbtotalEdge = sum(barbedgeBetweenness.values())
barbaverageEdge = sum(
barbedgeBetweenness.values()) / len(barbedgeBetweenness)
barbtotalEdge = sum(barbedgeBetweenness.values())
print("\n")
print("\n")
print("Scores for Cut Distance: ", cutDistance[a])
print("\n")
# print sets of nodes, one for each community.
print("Communities: ", barbMod)
print("\n")
# Modularity Score
print("Modularity: ", barbModScore)
print("\n")
# Conductance Score
sumOfCond = []
for i in range(len(clusterL)):
if clusterL[i] == []:
# catch Division by zero
continue
# calculate Cond for current Cluster in list of Clusters
currentCond = conductance(new_graph, clusterL[i])
sumOfCond.append(currentCond)
print("Conductance for: ", clusterL[i], " = ", currentCond)
overallCond = min(sumOfCond)
print("Overall Conductance: ", overallCond)
print("\n")
# edge_betweenness_centrality
print("Edge Betweenness Centrality Score: ", barbaverageEdge)
from networkx.algorithms.cuts import conductance
from networkx.algorithms.community import quality as qu
# barbell_graph
barb = nx.barbell_graph(4, 0)
nx.draw(barb, with_labels=True, font_weight='bold')
S = [0, 1, 2, 3]
T = [4, 5, 6, 7]
cluster = [[0, 1, 2, 3], [4, 5, 6, 7]]
# Conductance
sumOfCond = []
for i in range(len(cluster)):
sumOfCond.append(conductance(barb, cluster[i]))
condScoreS = conductance(barb, S)
condScoreT = conductance(barb, T)
overallCond = min(sumOfCond)
# Modularity Communities
barbMod = list(greedy_modularity_communities(barb))
# Modularity Score
barbModScore = qu.modularity(barb, barbMod)
# edge_betweenness_centrality
barbedgeBetweenness = nx.edge_betweenness_centrality(barb, None, False)
barbaverageEdge = sum(barbedgeBetweenness.values()) / len(barbedgeBetweenness)
barbtotalEdge = sum(barbedgeBetweenness.values())
# Modularity Score
print("Modularity: ", ModScore)
print("\n")
modularityEnsemble.update({cutDistance[a]: ModScore})
# Conductance Score
sumOfCond = []
for i in range(len(clusterL)):
if clusterL[i] == []:
# catch Division by zero
continue
if nx.volume(original_graph, clusterL[i]) == 0:
continue
# calculate Cond for current Cluster in list of Clusters
currentCond = conductance(original_graph, clusterL[i])
sumOfCond.append(currentCond)
print("Conductance for: ", clusterL[i], " = ", currentCond)
overallCond = min(sumOfCond)
print("Overall Conductance: ", overallCond)
print("\n")
conductanceEnsemble.update({cutDistance[a]: overallCond})
# edge betweenness centrality Score
print("Edge Betweenness Centrality Score: ", averageEdge)
print("Edge Betweenness Centrality Score: ", totalEdge)
edgeEnsemble.update({cutDistance[a]: totalEdge})
bestModCut = max(modularityEnsemble, key=modularityEnsemble.get)
# Draw the resulting graph
nx.draw(my_graph, with_labels=True, font_weight='bold')
# Modularity Communities
c = list(greedy_modularity_communities(my_graph))
# Modularity Score
modScore = qu.modularity(my_graph, c)
# Conductance
# S (sequence) – A sequence of nodes in my_graph.
# T (sequence) – A sequence of nodes in my_graph.
S = [0, 1]
T = [2, 3, 4]
condScore = conductance(my_graph, S, T)
# edge_betweenness_centrality
edgeBetweenness = nx.edge_betweenness_centrality(my_graph)
averageEdge = sum(edgeBetweenness.values()) / len(edgeBetweenness)
totalEdge = sum(edgeBetweenness.values())
# print sets of nodes, one for each community.
print("Communities: ", c)
# Modularity Score
print("Modularity: ", modScore)
# Conductance Score
print("Conductance: ", condScore)
import networkx.algorithms.cuts as cuts
import networkx.algorithms.community as community
# Generally, the lower value of
# conductance the better quality the cluster is (Cai 2011).
conductance_list_M = []
part_set_M = []
for i in range(len(prS_modified)):
p = i
while p < len(prS_modified):
conductance_list_M.append(
cuts.conductance(G2, prS_modified[i],
prS_modified[p]))
p += 1
conductance_list = []
for i in range(len(prS)):
p = i + 1
while p < len(prS):
conductance_list.append(
cuts.conductance(G2, prS[i],
prS[p]))
p += 1
print(sum(conductance_list_M))
print(sum(conductance_list))