def write_cluster_csv(btw_data, graph_data, file_pre):
clusters = {}
iqr_vals = []
for q in btw_data.keys():
iqr_vals.append(q)
for n in graph_data[iqr_vals[0]].nodes:
if graph_data[iqr_vals[0]].nodes[n]["cluster"] not in clusters:
clusters[graph_data[iqr_vals[0]].nodes[n]["cluster"]] = 1
else:
clusters[graph_data[iqr_vals[0]].nodes[n]["cluster"]] += 1
retstr = 'cluster, size, group_betweenness_centrality_25, group_betweenness_centrality_50, group_betweenness_centrality_75\n'
for c in clusters:
cnodes = [x for x,y in graph_data[iqr_vals[0]].nodes(data=True) if y['cluster']== c]
c_obj = {
"cluster" : c,
"size" : clusters[c],
"group_betweenness_25" : nx.group_betweenness_centrality(graph_data[iqr_vals[0]], cnodes, weight="score"),
"group_betweenness_50" : 0,#nx.group_betweenness_centrality(graph_data[iqr_vals[1]], cnodes, weight="score"),
"group_betweenness_75" : 0#nx.group_betweenness_centrality(graph_data[iqr_vals[2]], cnodes, weight="score")
}
lstr = '{}, {}, {}, {}, {}\n'.format(c_obj["cluster"], c_obj["size"], c_obj["group_betweenness_25"], c_obj["group_betweenness_50"], c_obj["group_betweenness_75"])
retstr = retstr + lstr
with open('centrality_files/' + file_pre + "_clusters.csv", 'w') as wf:
wf.write(retstr)
return
def test_group_betweenness_value_zero(self):
"""
Group betweenness centrality value of 0
"""
G = nx.cycle_graph(6)
C = [0, 1, 5]
b = nx.group_betweenness_centrality(G, C, weight=None)
b_answer = 0.0
assert b == b_answer
def test_group_betweenness_single_node(self):
"""
Group betweenness centrality for single node group
"""
G = nx.path_graph(5)
C = [1]
b = nx.group_betweenness_centrality(G, C, weight=None, normalized=False)
b_answer = 3.0
assert b == b_answer
def test_two_group_betweenness_value_zero(self):
"""
Group betweenness centrality value of 0
"""
G = nx.cycle_graph(7)
C = [[0, 1, 6], [0, 1, 5]]
b = nx.group_betweenness_centrality(G, C, weight=None, normalized=False)
b_answer = [0.0, 3.0]
assert b == b_answer
def test_group_betweenness_value_zero(self):
"""
Group betweenness centrality value of 0
"""
G = nx.cycle_graph(6)
C = [0, 1, 5]
b = nx.group_betweenness_centrality(G, C, weight=None)
b_answer = 0.0
assert_equal(b, b_answer)
def test_group_betweenness_disconnected_graph(self):
"""
Group betweenness centrality in a disconnected graph
"""
G = nx.path_graph(5)
G.remove_edge(0, 1)
C = [1]
b = nx.group_betweenness_centrality(G, C, weight=None)
b_answer = 0.0
assert b == b_answer
def test_group_betweenness_normalized(self):
"""
Group betweenness centrality for group with more than
1 node and normalized
"""
G = nx.path_graph(5)
C = [1, 3]
b = nx.group_betweenness_centrality(G, C, weight=None, normalized=True)
b_answer = 1.0
assert b == b_answer
def test_group_betweenness_disconnected_graph(self):
"""
Group betweenness centrality in a disconnected graph
"""
G = nx.path_graph(5)
G.remove_edge(0, 1)
C = [1]
b = nx.group_betweenness_centrality(G, C, weight=None)
b_answer = 0.0
assert_equal(b, b_answer)
def test_group_betweenness_single_node(self):
"""
Group betweenness centrality for single node group
"""
G = nx.path_graph(5)
C = [1]
b = nx.group_betweenness_centrality(G, C,
weight=None, normalized=False)
b_answer = 3.0
assert_equal(b, b_answer)
def test_group_betweenness_with_endpoints(self):
"""
Group betweenness centrality for single node group
"""
G = nx.path_graph(5)
C = [1]
b = nx.group_betweenness_centrality(
G, C, weight=None, normalized=False, endpoints=True
)
b_answer = 7.0
assert b == b_answer
def test_group_betweenness_normalized(self):
"""
Group betweenness centrality for group with more than
1 node and normalized
"""
G = nx.path_graph(5)
C = [1, 3]
b = nx.group_betweenness_centrality(G, C,
weight=None, normalized=True)
b_answer = 1.0
assert_equal(b, b_answer)
def groupBetweenCentrality(G):
pos = nx.spring_layout(G)
gbCent = nx.group_betweenness_centrality(G)
node_color = [20000.0 * G.degree(v) for v in G]
node_size = [v * 10000 for v in gbCent.values()]
plt.figure(figsize=(13, 13))
nx.draw_networkx(G1,
pos=pos,
with_labels=False,
node_color=node_color,
node_size=node_size)
plt.axis('off')
#Printing Top 10 Nodes as per Closeness Centrality
sorted_degree = sorted(gbCent, key=gbCent.get, reverse=True)[:10]
for d in sorted_degree[:10]:
print("Node Label: ", d, "=> Degree: ", G.degree[d])
def test_group_betweenness_directed_weighted(self):
"""
Group betweenness centrality in a directed and weighted graph
"""
G = nx.DiGraph()
G.add_edge(1, 0, weight=1)
G.add_edge(0, 2, weight=2)
G.add_edge(1, 2, weight=3)
G.add_edge(3, 1, weight=4)
G.add_edge(2, 3, weight=1)
G.add_edge(4, 3, weight=6)
G.add_edge(2, 4, weight=7)
C = [1, 2]
b = nx.group_betweenness_centrality(G, C, weight="weight", normalized=False)
b_answer = 5.0
assert b == b_answer
def test_group_betweenness_node_not_in_graph(self):
"""
Node(s) in C not in graph, raises NodeNotFound exception
"""
with pytest.raises(nx.NodeNotFound):
b = nx.group_betweenness_centrality(nx.path_graph(5), [6, 7, 8])
