def sortNodes(path, graph, comms, partition, type_sorting):
if path is None and graph is None:
return ()
g = nx.read_gpickle(path) if path is not None else graph
degrees_x = []
degrees_y = []
sorted_x = []
sorted_y = []
#in_degree
if type_sorting == 0:
degrees_x = g.in_degree(comms[0]) #comm X -- to be ordered
degrees_y = g.in_degree(comms[1]) #comm Y -- to be ordered
sorted_x = sorted(degrees_x ,key=lambda tup: tup[1], reverse=True)
sorted_y = sorted(degrees_y ,key=lambda tup: tup[1], reverse=True)
#ratio
elif type_sorting == 1:
for i in comms[0]:
degrees_x.append((i,g.in_degree(i)/(g.out_degree(i)+1)))
for j in comms[1]:
degrees_y.append((j,g.in_degree(j)/(g.out_degree(j)+1)))
sorted_x = sorted(degrees_x ,key=lambda tup: tup[1], reverse=True)
sorted_y = sorted(degrees_y ,key=lambda tup: tup[1], reverse=True)
#betweenness centrality
elif type_sorting == 2:
centrality_x = nx.betweenness_centrality_subset(g, comms[0], comms[1], normalized=True)
centrality_y = nx.betweenness_centrality_subset(g, comms[1], comms[0], normalized=True)
sorted_x = sorted([i for i in centrality_x.iteritems() if partition[i[0]] == 0], key=lambda (k,v):(v,k), reverse=False)
sorted_y = sorted([i for i in centrality_y.iteritems() if partition[i[0]] == 1], key=lambda (k,v):(v,k), reverse=False)
else:
for i in comms[0]:
degrees_x.append((i,AvgInDegree(i, g)))
for j in comms[1]:
degrees_y.append((j,AvgInDegree(j, g)))
sorted_x = sorted(degrees_x ,key=lambda tup: tup[1], reverse=True)
sorted_y = sorted(degrees_y ,key=lambda tup: tup[1], reverse=True)
return (sorted_x, sorted_y)
def bet_subset():
graph = {
"nodes": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],
"edges": [[1, 2], [1, 4], [2, 3], [2, 5], [3, 4], [3, 6], [2, 7],
[3, 8], [7, 9], [5, 9], [9, 10], [10, 6]],
"weights": [3, 4, 5, 6, 7, 8, 9, 10]
}
# G = nx.Graph()
G = nx.DiGraph()
G.add_nodes_from(graph['nodes'])
G.add_edges_from(graph['edges'])
source_nodes = [5, 7]
target_nodes = [6]
result = nx.betweenness_centrality_subset(G, source_nodes, target_nodes,
False)
print(result)
# print(list(max(result.items(), key=lambda k: k[1])))
# print(result.items())
# print(max(result.items(), key=operator.itemgetter(1)))
highest = max(result.values())
print(highest)
print([k for k, v in result.items() if v == highest])
result = nx.edge_betweenness_centrality_subset(G, source_nodes,
target_nodes, False)
print(result)
highest = max(result.values())
print(highest)
print([k for k, v in result.items() if v == highest])
def dummy_edge_mix_BC_weighted_choice(G,node_to_add,destination_node):
#average degree
D = list(map(lambda x : x[1], list(G.degree(G.nodes))))
edge_to_add = int(np.mean(D)*0.5)
DegreeDict = {};
DummyDegreeDict = {};
#calculate betweenness centrality.
Bdict = nx.betweenness_centrality_subset(G,sources=set(G.nodes()),targets=set({destination_node}))
B = []
for key in Bdict.keys():
B.append((key,Bdict[key]))
DegreeDict[key] = G.degree[key];
DummyDegreeDict[key] = 0;
#Weighted k-sampling
for dummy in range(0, node_to_add):
Bdg = []
for element in B:
m = DegreeDict[element[0]]
n = DummyDegreeDict[element[0]]
Bdg.append((element[0],element[1]))
target_nodes1 = random.choices(list(map(lambda x : x[0], Bdg)),weights=list(map(lambda x : x[1], Bdg)),k=edge_to_add)
target_nodes2 = random.choices(list(map(lambda x : x[0], Bdg)),weights=list(map(lambda x : 1-x[1], Bdg)),k=edge_to_add)
while len(list(set(target_nodes1).intersection(target_nodes2))) > 0:
target_nodes1 = random.choices(list(map(lambda x : x[0], Bdg)),weights=list(map(lambda x : x[1], Bdg)),k=edge_to_add)
target_nodes2 = random.choices(list(map(lambda x : x[0], Bdg)),weights=list(map(lambda x : 1-x[1], Bdg)),k=edge_to_add)
target_nodes = target_nodes1+target_nodes2
#add dummy node
G.add_node(G.number_of_nodes())
dummyNode = G.number_of_nodes()-1
#add dummy edge
for target in target_nodes:
G.add_edge(target,dummyNode);
G.add_edge(dummyNode,target);
DummyDegreeDict[target]+=1;
def get_path_subgraph(self, sources, targets):
"""Return an induced subgraph of all paths connecting two sets of nodes.
Parameters
----------
sources : list
A list of node names to be used as path sources.
targets : list
A list of node names to be used as path targets.
Returns
-------
networkx.Graph
An induced subgraph that contains all nodes and edges between ``sources`` and ``targets``.
"""
bc = networkx.betweenness_centrality_subset(self,
sources=sources,
targets=targets)
path_nodes = [
key for key, value in bc.items()
if value > 0 or key in sources or key in targets
]
return (self.subgraph(path_nodes))
def getNodeSpecBetweenness():
global outfile
global data
global source
global sink
global distance
global dist_mat
weight = []
sources = []
sinks = []
sinks.append(sink)
for i in range(0, len(dist_mat[source])):
if dist_mat[source, i] <= distance:
sources.append(i)
print "sources are: ", sources
print "sinks are: ", sinks
G = nx.from_numpy_matrix(data)
for i in xrange(len(data)):
for j in xrange(i + 1, len(data)):
if (data[i, j] != 0):
weight.append(data[i, j])
G = nx.from_numpy_matrix(data)
net = nx.betweenness_centrality_subset(G,
sources=sources,
targets=sinks,
normalized=False,
weight="weight")
f = open(outfile, 'w')
with open(outfile, 'w') as f:
for key, value in net.items():
f.write('%s %s\n' % (key, value))
def test_K5(self):
"""Betweenness centrality: K5"""
G = networkx.complete_graph(5)
b = betweenness_centrality_subset(G, sources=[0], targets=[1, 3], weighted_edges=False)
b_answer = {0: 0.0, 1: 0.0, 2: 0.0, 3: 0.0, 4: 0.0}
for n in sorted(G):
assert_almost_equal(b[n], b_answer[n])
def choose_who_to_vaccinate(graph: networkx.Graph) -> List:
people_to_vaccinate = []
# T = networkx.algorithms.maximum_spanning_tree(graph)
node2degree = dict(graph.degree)
sorted_nodes = sorted(node2degree.items(),
key=lambda item: item[1],
reverse=True)[:100]
l1 = [node[0] for node in sorted_nodes]
g2 = graph.subgraph(l1)
l2 = networkx.closeness_centrality(g2)
sorted_by_closness = sorted(l2.items(),
key=lambda item: item[1],
reverse=True)[:70]
l3 = [i[0] for i in sorted_by_closness]
g3 = graph.subgraph(l3)
contenders = networkx.betweenness_centrality_subset(g3,
l3,
l3,
normalized=True)
sorted_nodes = sorted(contenders.items(),
key=lambda item: item[1],
reverse=True)[:50]
people_to_vaccinate = [i[0] for i in sorted_nodes]
patientsRand = []
graph.remove_nodes_from(
[n for n in graph if n in set(people_to_vaccinate)])
for i in range(50):
patientsRand.append(np.random.choice(graph.nodes()))
# ICM(graph, patientsRand, 6)
return people_to_vaccinate
def _betmap(G_normalized_weight_sources_tuple):
"""Pool for multiprocess only accepts functions with one argument.
This function uses a tuple as its only argument. We use a named tuple for
python 3 compatibility, and then unpack it when we send it to
`betweenness_centrality_source`
"""
return nx.betweenness_centrality_subset(*G_normalized_weight_sources_tuple)
def test_diamond_multi_path(self):
"""Betweenness Centrality Subset: Diamond Multi Path"""
G = nx.Graph()
G.add_edges_from([(1, 2), (1, 3), (1, 4), (1, 5), (1, 10), (10, 11),
(11, 12), (12, 9), (2, 6), (3, 6), (4, 6), (5, 7),
(7, 8), (6, 8), (8, 9)])
b = nx.betweenness_centrality_subset(G,
sources=[1],
targets=[9],
weight=None)
expected_b = {
1: 0,
2: 1. / 10,
3: 1. / 10,
4: 1. / 10,
5: 1. / 10,
6: 3. / 10,
7: 1. / 10,
8: 4. / 10,
9: 0,
10: 1. / 10,
11: 1. / 10,
12: 1. / 10,
}
for n in sorted(G):
assert_almost_equal(b[n], expected_b[n])
def test_P5_multiple_target(self):
"""Betweenness centrality: P5 multiple target"""
G = networkx.Graph()
G.add_path(range(5))
b_answer = {0: 0, 1: 1, 2: 1, 3: 0.5, 4: 0, 5: 0}
b = betweenness_centrality_subset(G, sources=[0], targets=[3, 4], weighted_edges=False)
for n in sorted(G):
assert_almost_equal(b[n], b_answer[n])
def test_K5(self):
"""Betweenness Centrality Subset: K5"""
G = nx.complete_graph(5)
b = nx.betweenness_centrality_subset(G, sources=[0], targets=[1, 3],
weight=None)
b_answer = {0: 0.0, 1: 0.0, 2: 0.0, 3: 0.0, 4: 0.0}
for n in sorted(G):
assert almost_equal(b[n], b_answer[n])
def test_P5_multiple_target(self):
"""Betweenness Centrality Subset: P5 multiple target"""
G = nx.Graph()
nx.add_path(G, range(5))
b_answer = {0: 0, 1: 1, 2: 1, 3: 0.5, 4: 0, 5: 0}
b = nx.betweenness_centrality_subset(G, sources=[0], targets=[3, 4],
weight=None)
for n in sorted(G):
assert almost_equal(b[n], b_answer[n])
def test_box(self):
"""Betweenness centrality: box"""
G = nx.Graph()
G.add_edges_from([(0, 1), (0, 2), (1, 3), (2, 3)])
b_answer = {0: 0, 1: 0.25, 2: 0.25, 3: 0}
b = nx.betweenness_centrality_subset(G, sources=[0], targets=[3],
weight=None)
for n in sorted(G):
assert_almost_equal(b[n], b_answer[n])
def test_box_and_path2(self):
"""Betweenness centrality: box and path multiple target"""
G = nx.Graph()
G.add_edges_from([(0, 1), (1, 2), (2, 3), (1, 20), (20, 3), (3, 4)])
b_answer = {0: 0, 1: 1.0, 2: 0.5, 20: 0.5, 3: 0.5, 4: 0}
b = nx.betweenness_centrality_subset(G, sources=[0], targets=[3, 4],
weight=None)
for n in sorted(G):
assert_almost_equal(b[n], b_answer[n])
def most_important_nodes_edges_subset(self, graph, source_nodes, target_nodes, T=0, normalized=False, directed=False):
cv=check_graph_validity.Graphs()
ret = cv.is_valid_most_important_graph(graph, source_nodes, target_nodes,T)
if(not ret[0]):
ret.append({})
print (ret)
return ret
try:
# construct networkx graph from given graph
if (directed):
G=nx.DiGraph()
else:
G = nx.Graph()
G.add_nodes_from(graph['nodes'])
G.add_edges_from(graph['edges'])
if 'weights' in graph:
for i in range(len(graph['edges'])):
G[graph['edges'][i][0]][graph['edges'][i][1]]['weight'] = graph['weights'][i]
except Exception as e:
return ["False", str(e),{}]
output={}
# clearDict={}
result = None
if (T == 0):
result=nx.betweenness_centrality_subset(G, source_nodes, target_nodes, normalized, weight='weights')
#remove nodes that are either in source_node or in target_node
# for key,val in result.items():
# if (not(key in source_nodes or key in target_nodes)):
# clearDict[key]=val
# print(clearDict)
elif (T == 1):
result =nx.edge_betweenness_centrality_subset(G, source_nodes, target_nodes, normalized, weight='weights')
# for key,val in result.items():
# #remove nodes that are either in source_node or in target_node
# if(not(key[0] in source_nodes or key[0] in target_nodes or key[1] in source_nodes or key[1] in target_nodes)):
# clearDict[key]=val
# print(clearDict)
# output["betweenness_centrality"] = list(max(clearDict.items(), key=lambda k: k[1]))
highest = max(result.values())
nodes_list = [k for k, v in result.items() if v == highest]
output["betweenness_centrality"] = [nodes_list,highest] #result
print (output)
return [True, 'success', output]
def test_P5(self):
"""Betweenness centrality: P5"""
G = nx.Graph()
nx.add_path(G, range(5))
b_answer = {0: 0, 1: 0.5, 2: 0.5, 3: 0, 4: 0, 5: 0}
b = nx.betweenness_centrality_subset(G, sources=[0], targets=[3],
weight=None)
for n in sorted(G):
assert_almost_equal(b[n], b_answer[n])
def test_box(self):
"""Betweenness Centrality Subset: box"""
G = nx.Graph()
G.add_edges_from([(0, 1), (0, 2), (1, 3), (2, 3)])
b_answer = {0: 0, 1: 0.25, 2: 0.25, 3: 0}
b = nx.betweenness_centrality_subset(G, sources=[0], targets=[3],
weight=None)
for n in sorted(G):
assert almost_equal(b[n], b_answer[n])
def test_box_and_path2(self):
"""Betweenness Centrality Subset: box and path multiple target"""
G = nx.Graph()
G.add_edges_from([(0, 1), (1, 2), (2, 3), (1, 20), (20, 3), (3, 4)])
b_answer = {0: 0, 1: 1.0, 2: 0.5, 20: 0.5, 3: 0.5, 4: 0}
b = nx.betweenness_centrality_subset(G, sources=[0], targets=[3, 4],
weight=None)
for n in sorted(G):
assert almost_equal(b[n], b_answer[n])
def test_P5_multiple_target(self):
"""Betweenness Centrality Subset: P5 multiple target"""
G = nx.Graph()
nx.add_path(G, range(5))
b_answer = {0: 0, 1: 1, 2: 1, 3: 0.5, 4: 0, 5: 0}
b = nx.betweenness_centrality_subset(G, sources=[0], targets=[3, 4],
weight=None)
for n in sorted(G):
assert_almost_equal(b[n], b_answer[n])
def test_P5_directed(self):
"""Betweenness Centrality Subset: P5 directed"""
G = nx.DiGraph()
nx.add_path(G, range(5))
b_answer = {0: 0, 1: 1, 2: 1, 3: 0, 4: 0, 5: 0}
b = nx.betweenness_centrality_subset(G, sources=[0], targets=[3],
weight=None)
for n in sorted(G):
assert_almost_equal(b[n], b_answer[n])
def Btw(self, U, rho):
max = -1
node = 0
btw = nx.betweenness_centrality_subset(self.G, U, self.G.nodes(),
False, None)
for u in U:
if btw[u] > max:
max = btw[u]
node = u
return node
def test_K5(self):
"""Betweenness centrality: K5"""
G = networkx.complete_graph(5)
b = betweenness_centrality_subset(G,
sources=[0],
targets=[1, 3],
weighted_edges=False)
b_answer = {0: 0.0, 1: 0.0, 2: 0.0, 3: 0.0, 4: 0.0}
for n in sorted(G):
assert_almost_equal(b[n], b_answer[n])
def test_box(self):
"""Betweenness centrality: box"""
G = networkx.Graph()
G.add_edge(0, 1)
G.add_edge(0, 2)
G.add_edge(1, 3)
G.add_edge(2, 3)
b_answer = {0: 0, 1: 0.25, 2: 0.25, 3: 0}
b = betweenness_centrality_subset(G, sources=[0], targets=[3], weighted_edges=False)
for n in sorted(G):
assert_almost_equal(b[n], b_answer[n])
def test_P5_multiple_target(self):
"""Betweenness centrality: P5 multiple target"""
G = networkx.Graph()
G.add_path(list(range(5)))
b_answer = {0: 0, 1: 1, 2: 1, 3: 0.5, 4: 0, 5: 0}
b = betweenness_centrality_subset(G,
sources=[0],
targets=[3, 4],
weighted_edges=False)
for n in sorted(G):
assert_almost_equal(b[n], b_answer[n])
def test_box_and_path(self):
"""Betweenness Centrality Subset: box and path"""
G = nx.Graph()
G.add_edges_from([(0, 1), (0, 2), (1, 3), (2, 3), (3, 4), (4, 5)])
b_answer = {0: 0, 1: 0.5, 2: 0.5, 3: 0.5, 4: 0, 5: 0}
b = nx.betweenness_centrality_subset(G,
sources=[0],
targets=[3, 4],
weight=None)
for n in sorted(G):
assert b[n] == pytest.approx(b_answer[n], abs=1e-7)
def test_P5(self):
"""Betweenness centrality: P5"""
G = nx.Graph()
nx.add_path(G, range(5))
b_answer = {0: 0, 1: 0.5, 2: 0.5, 3: 0, 4: 0, 5: 0}
b = betweenness_centrality_subset(G,
sources=[0],
targets=[3],
weight=None)
for n in sorted(G):
assert_almost_equal(b[n], b_answer[n])
def test_P5(self):
"""Betweenness Centrality Subset: P5"""
G = nx.Graph()
nx.add_path(G, range(5))
b_answer = {0: 0, 1: 0.5, 2: 0.5, 3: 0, 4: 0, 5: 0}
b = nx.betweenness_centrality_subset(G,
sources=[0],
targets=[3],
weight=None)
for n in sorted(G):
assert b[n] == pytest.approx(b_answer[n], abs=1e-7)
def test_box_and_path(self):
"""Betweenness centrality: box and path"""
G = nx.Graph()
G.add_edges_from([(0, 1), (0, 2), (1, 3), (2, 3), (3, 4), (4, 5)])
b_answer = {0: 0, 1: 0.5, 2: 0.5, 3: 0.5, 4: 0, 5: 0}
b = nx.betweenness_centrality_subset(G,
sources=[0],
targets=[3, 4],
weight=None)
for n in sorted(G):
assert_almost_equal(b[n], b_answer[n])
def test_P5_directed(self):
"""Betweenness centrality: P5 directed"""
G=networkx.DiGraph()
G.add_path(list(range(5)))
b_answer={0:0,1:1,2:1,3:0,4:0,5:0}
b=betweenness_centrality_subset(G,
sources=[0],
targets=[3],
weight=None)
for n in sorted(G):
assert_almost_equal(b[n],b_answer[n])
def all_tftg_candidates(instance):
nx_obj, dict_tf_comm, dict_tg_comm, tf_id, tg_id = instance[0], instance[1], instance[2], instance[3], instance[4]
tfs, tgs = dict_tf_comm[tf_id], dict_tg_comm[tg_id]
tfs_common, tgs_common = set(tfs).intersection(nx_obj.nodes()), set(tgs).intersection(nx_obj.nodes())
dict_cent_allnodes=nx.betweenness_centrality_subset(nx_obj,tfs_common,tgs_common)
set_nodes = set(pd.DataFrame.from_dict(dict_cent_allnodes,orient='index',columns=['paths']).loc[lambda x:x.paths !=0].index.to_list())
nx_obj = nx_obj.subgraph(set_nodes.union(tfs).union(tgs)) ###########
dict_res = {}
li_li_edges = pd.DataFrame(nx_obj.edges).values.tolist()
set_tup_edges = [(i1,i2) for i1,i2 in li_li_edges]
dict_res[(tf_id,tg_id)] = set_tup_edges
return dict_res
def function(sample):
pnm_params = {
'data_path': r"Z:\Robert_TOMCAT_3_netcdf4_archives\for_PNM",
# 'data_path': r"A:\Robert_TOMCAT_3_netcdf4_archives\processed_1200_dry_seg_aniso_sep",
'sample': sample
# 'sample': 'T3_100_7_III',
# 'sample': 'T3_025_3_III',
# 'sample': 'T3_300_8_III',
}
pnm = PNM(**pnm_params)
graph = pnm.graph
sourcesraw = np.unique(pnm.data['label_matrix'][:, :, 0])[1:]
targetsraw = np.unique(pnm.data['label_matrix'][:, :, -1])[1:]
sources = []
targets = []
for k in sourcesraw:
if k in graph.nodes():
sources.append(k)
for k in targetsraw:
if k in graph.nodes():
targets.append(k)
centrality = np.array(list(nx.betweenness_centrality(graph).values()))
centrality2 = np.array(
list(
nx.betweenness_centrality_subset(graph,
sources,
targets,
normalized=True).values()))
centrality5 = np.array(
list(
nx.betweenness_centrality_source(graph, sources=sources).values()))
waiting_times = np.zeros(len(centrality))
vx = pnm.data.attrs['voxel']
V = pnm.data['volume'].sum(dim='label')
V0 = 0.95 * V.max()
ref = V[V < V0].argmax()
meanflux = (V0 * vx**3 / pnm.data['time'][ref])
# meanflux = V[-1]*vx**3/pnm.data['time'][-1]
meanflux = meanflux.data
i = 0
for node in graph.nodes():
wt = 0
nb = list(nx.neighbors(graph, node))
if len(nb) > 0:
t0 = pnm.data['sig_fit_data'].sel(label=node, sig_fit_var='t0 [s]')
t0nb = pnm.data['sig_fit_data'].sel(label=nb, sig_fit_var='t0 [s]')
upstream_nb = np.where(t0nb < t0)
if len(upstream_nb[0]) > 0:
wt = t0 - t0nb[upstream_nb].max()
waiting_times[i] = wt
i += 1
return centrality, waiting_times, meanflux, centrality5, centrality2, sample
def all_tftg_candidates(nx_obj, tfs, tgs):
tfs_common, tgs_common = set(tfs).intersection(nx_obj.nodes()), set(tgs).intersection(nx_obj.nodes())
dict_cent_allnodes=nx.betweenness_centrality_subset(nx_obj,tfs_common,tgs_common)
set_nodes = set(pd.DataFrame.from_dict(dict_cent_allnodes,orient='index',columns=['paths']).loc[lambda x:x.paths !=0].index.to_list()) ##########
nx_obj = nx_obj.subgraph(set_nodes.union(tfs).union(tgs)) ###########
dict_tftgs = nx.to_dict_of_lists(nx_obj)
dict_tftgs_filtered ={}
for key in dict_tftgs:
if len(dict_tftgs[key])==0:
continue
else:
dict_tftgs_filtered[key] = dict_tftgs[key]
return nx_obj, dict_tftgs_filtered
def test_box_and_path2(self):
"""Betweenness centrality: box and path multiple target"""
G = networkx.Graph()
G.add_edge(0, 1)
G.add_edge(1, 2)
G.add_edge(2, 3)
G.add_edge(1, 20)
G.add_edge(20, 3)
G.add_edge(3, 4)
b_answer = {0: 0, 1: 1.0, 2: 0.5, 20: 0.5, 3: 0.5, 4: 0}
b = betweenness_centrality_subset(G, sources=[0], targets=[3, 4])
for n in sorted(G):
assert_almost_equal(b[n], b_answer[n])
def test_box_and_path2(self):
"""Betweenness centrality: box and path multiple target"""
G = networkx.Graph()
G.add_edge(0, 1)
G.add_edge(1, 2)
G.add_edge(2, 3)
G.add_edge(1, 20)
G.add_edge(20, 3)
G.add_edge(3, 4)
b_answer = {0: 0, 1: 1.0, 2: 0.5, 20: 0.5, 3: 0.5, 4: 0}
b = betweenness_centrality_subset(G, sources=[0], targets=[3, 4])
for n in sorted(G):
assert_almost_equal(b[n], b_answer[n])
def prune(self,
remove_singletons=True,
trim_leaves=False,
remove_placeholders=False,
sources=None,
targets=None):
"""Remove specific parts of the graph.
Parameters
----------
remove_singletons : bool, optional
Should singletons be removed? [default: True]
trim_leaves : bool, optional
Iteratively remove all nodes with degree one, effectively
keeping only nodes that are in cycles? [default: False]
remove_placeholders : bool, optional
Should placeholder nodes be removed? [default: False]
sources, targets : list, optional
Lists of node names. If provided, nodes that are not on any path
between ``sources`` and ``targets`` will be removed. [default: None]
"""
if remove_placeholders == True:
self.remove_nodes_from(self.placeholders)
if sources is not None and targets is not None:
bc = networkx.betweenness_centrality_subset(self,
sources=sources,
targets=targets)
remove_bc = [
key for key, value in bc.items()
if value == 0 and not key in sources and not key in targets
]
self.remove_nodes_from(remove_bc)
if trim_leaves:
degrees = dict(self.degree())
while 1 in degrees.values():
for key, value in degrees.items():
if (value == 1):
self.remove_node(key)
degrees = dict(self.degree())
if remove_singletons:
degrees = dict(self.degree())
for key, value in degrees.items():
if (value == 0):
self.remove_node(key)
def test_box(self):
"""Betweenness centrality: box"""
G = networkx.Graph()
G.add_edge(0, 1)
G.add_edge(0, 2)
G.add_edge(1, 3)
G.add_edge(2, 3)
b_answer = {0: 0, 1: 0.25, 2: 0.25, 3: 0}
b = betweenness_centrality_subset(G,
sources=[0],
targets=[3],
weighted_edges=False)
for n in sorted(G):
assert_almost_equal(b[n], b_answer[n])
def test_box_and_path(self):
"""Betweenness centrality: box and path"""
G=networkx.Graph()
G.add_edge(0,1)
G.add_edge(0,2)
G.add_edge(1,3)
G.add_edge(2,3)
G.add_edge(3,4)
G.add_edge(4,5)
b_answer={0:0,1:0.5,2:0.5,3:0.5,4:0,5:0}
b=betweenness_centrality_subset(G,
sources=[0],
targets=[3,4],
weight=None)
for n in sorted(G):
assert_almost_equal(b[n],b_answer[n])
def getNodeSpecBetweenness():
global outfile
global data
global sources
global sinks
weight = []
G=nx.from_numpy_matrix(data)
for i in xrange(len(data)):
for j in xrange(i+1,len(data)):
if(data[i,j] != 0):
weight.append(data[i,j])
G=nx.from_numpy_matrix(data)
net=nx.betweenness_centrality_subset(G,sources=sources,targets=sinks,normalized=False,weight="weight")
f=open(outfile, 'w')
with open(outfile, 'w') as f:
for key, value in net.items():
f.write('%s %s\n' % (key, value))
def test_diamond_multi_path(self):
"""Betweenness Centrality Subset: Diamond Multi Path"""
G = nx.Graph()
G.add_edges_from([
(1,2),
(1,3),
(1,4),
(1,5),
(1,10),
(10,11),
(11,12),
(12,9),
(2,6),
(3,6),
(4,6),
(5,7),
(7,8),
(6,8),
(8,9)
])
b = nx.betweenness_centrality_subset(
G,
sources=[1],
targets=[9],
weight=None
)
expected_b = {
1: 0,
2: 1./10,
3: 1./10,
4: 1./10,
5: 1./10,
6: 3./10,
7:1./10,
8:4./10,
9:0,
10:1./10,
11:1./10,
12:1./10,
}
for n in sorted(G):
assert_almost_equal(b[n], expected_b[n])
def nx_betweenness_centrality(
graph: NetworkXGraph,
nodes: mg.Optional[PythonNodeSet],
normalize: bool,
# include_endpoints: bool,
) -> PythonNodeMap:
if nodes is None:
sources = targets = graph.value.nodes
else:
sources = targets = nodes.value
node_to_score_map = nx.betweenness_centrality_subset(
graph.value,
sources=sources,
targets=targets,
normalized=normalize,
weight=graph.edge_weight_label,
# endpoints=include_endpoints,
)
return PythonNodeMap(node_to_score_map, )
def betweeness_centrality_subset():
nodes = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]
edges_sources = [[1, 2], [1, 3], [3, 4], [4, 5], [5, 6], [6, 7]]
edges_targets = [[8, 9], [10, 11], [11, 12], [12, 13], [13, 14], [14, 15],
[10, 13], [9, 15], [8, 14]]
edges_interconnections = [[4, 8], [6, 10], [7, 14]
] # Connection between source and target nodes
edges = []
G = nx.Graph()
G.add_nodes_from(nodes)
G.add_edges_from(edges_sources)
G.add_edges_from(edges_targets)
G.add_edges_from(edges_interconnections)
# print(G.edges)
result = nx.betweenness_centrality_subset(G, [1, 2, 3, 4, 5],
[1, 13, 14, 15])
print(result)
result = nx.betweenness_centrality_subset(G, [
1,
2,
3,
], [13, 14, 15])
print(result)
result = nx.edge_betweenness_centrality_subset(G, [1, 2, 3, 4, 5],
[12, 13, 14, 15])
print(result)
print('--------------------------')
nodes = [1, 2, 3, 4, 5, 6, 7, 10, 11, 12, 13, 14, 15]
edges_sources = [[1, 2], [1, 3], [3, 4], [4, 6], [6, 7]] # 5 removed here
edges_targets = [[8, 9], [10, 11], [11, 13], [13, 14], [14, 15], [10, 13],
[9, 15], [8, 14]] # 12 removed here
edges_interconnections = [[4, 8], [6, 10], [7, 14]
] # Connection between source and target nodes
edges = []
G = nx.Graph()
G.add_nodes_from(nodes)
G.add_edges_from(edges_sources)
G.add_edges_from(edges_targets)
G.add_edges_from(edges_interconnections)
# print(G.edges)
result = nx.betweenness_centrality_subset(G, [1, 2, 3, 4, 5], [13, 14, 15])
print(result)
result = nx.betweenness_centrality_subset(G, [1, 2, 3, 4], [13, 14, 15])
print(result)
result = nx.edge_betweenness_centrality_subset(G, [1, 2, 3, 4, 5],
[12, 13, 14, 15])
print(result)
print('-------------------------')
print(G.edges)
G[1][2]['weight'] = 0.8
G[1][2]['weightre'] = 0.83
print(G[1][2])
