def allRW(G,p):
if p > 1:
p = 1
n = int(p*nx.number_of_nodes(G))
G1 = nx.Graph()
while nx.number_of_nodes(G1) < n:
count = 0
start_id = random.randint(0,nx.number_of_nodes(G)-1)
s_node = nx.nodes(G)[start_id]
now_node = s_node
while count < 5*n:
#print(now_node)
neighber_list = G.neighbors(now_node)
for node in neighber_list:
G1.add_edge(node,now_node)
next_id = random.randint(0,len(neighber_list)-1)
next_node = neighber_list[next_id]
# G1.add_edge(now_node,next_node)
count += 1
now_node = next_node
if nx.number_of_nodes(G1) >= n:
break
#print(now_node)
#print(G.neighbors(now_node))
return G1
def RWall2(G,p):
check = False
while not check:
count = 0
G1 = nx.Graph()
n = int(p*nx.number_of_nodes(G))
start_id = random.randint(0,nx.number_of_nodes(G)-1)
s_node = nx.nodes(G)[start_id]
G1.add_node(s_node)
now_node = s_node
while True:
neighbor_list = G.neighbors(now_node)
next_id = random.randint(0,len(neighbor_list)-1)
next_node = neighbor_list[next_id]
G1.add_node(next_node)
count += 1
if random.random() < 0:
next_id = random.randint(0,nx.number_of_nodes(G1)-1)
next_node = G1.nodes()[next_id]
now_node = next_node
if nx.number_of_nodes(G1) >= n:
check = True
break
if count > 50*n:
break
return NodeConnect(G,G1)
def BFS(G,p):
if p > 1:
p = 1
n = int(p*nx.number_of_nodes(G))
# print(n)
G1 = nx.Graph()
check = False
while nx.number_of_nodes(G1) < n:
process = []
start_id = random.randint(0,nx.number_of_nodes(G)-1)
s_node = nx.nodes(G)[start_id]
process.append(s_node)
######### print(process)
while True:
now_node = process[0]
neighbor = G.neighbors(now_node)
for next_node in neighbor:
if next_node not in G1.nodes():
G1.add_node(now_node)
G1.add_node(next_node)
process.append(next_node)
else:
G1.add_node(now_node)
G1.add_node(next_node)
process.remove(now_node)
if nx.number_of_nodes(G1)>=n:
check = True
break
if len(process) == 0:
break
if check:
break
return NodeConnect(G,G1)
def get_global_efficiency(filename):
import networkx as nx
threshold = 0
f = open(filename[:-4] + "_global_efficiency.dat", "w")
g = open(filename[:-4] + "_node_global_efficiency.dat", "w")
for i in range(0, 101):
threshold = float(i) / 100
G = get_threshold_matrix(filename, threshold)
global_efficiency = 0.0
for node_i in G:
sum_inverse_dist = 0.0
for node_j in G:
if node_i != node_j:
if nx.has_path(G, node_i, node_j) == True:
sum_inverse_dist += 1.0 / nx.shortest_path_length(G, node_i, node_j)
g.write("%d\t%f\t%f\n" % ((node_i + 1), threshold, (sum_inverse_dist / nx.number_of_nodes(G))))
global_efficiency += sum_inverse_dist / (nx.number_of_nodes(G) - 1.0)
g.write("\n")
global_efficiency = global_efficiency / nx.number_of_nodes(G)
f.write("%f\t%f\n" % (threshold, global_efficiency))
print "global efficiency for threshold %f: %f " % (threshold, global_efficiency)
f.close()
g.close()
def eccentricityAttributes(graph): return_values = [] #Average effective eccentricity eccVals = [] e = 0 for n in graph.nodes(): try: eccVals.append(nx.eccentricity(graph, v=n)) except nx.NetworkXError: eccVals.append(0) eccSum = 0 center_nodes = 0 phobic = 0 diameter = max(eccVals) radius = min(eccVals) for i in range(len(eccVals)): if eccVals[i] == radius: center_nodes += 1 if graph.node[i]['hydro'] == 'phobic': phobic += 1 eccSum += eccVals[i] return_values.append(eccSum / float(nx.number_of_nodes(graph))) #Effective diameter return_values.append(diameter) #Effective radius return_values.append(radius) #Percentage central nodes return_values.append(center_nodes / float(nx.number_of_nodes(graph))) #Percentage central nodes that are hydrophobic return_values.append(phobic / float(center_nodes)) return return_values
def fast_search(G, F, k, n, ind): start = numpy.ones(networkx.number_of_nodes(G)) C = laplacian_complete(networkx.number_of_nodes(G)) A = weighted_adjacency_complete(G, F, ind) CAC = fast_cac(G, F, ind) return simple_spectral_cut(CAC, start, F, G, 1., k, n, ind)
def algorithm(w1,w2,w3,w4,G1,G2,G3,G4):
try:
cc=np.array([nx.average_clustering(G1,weight='weight'),nx.average_clustering(G2,weight='weight'),nx.average_clustering(G3,weight='weight'),nx.average_clustering(G4,weight='weight')])
spl=np.array([nx.average_shortest_path_length(G1,weight='weight'),nx.average_shortest_path_length(G2,weight='weight'),nx.average_shortest_path_length(G3,weight='weight'),nx.average_shortest_path_length(G4,weight='weight')])
nds=np.array([nx.number_of_nodes(G1),nx.number_of_nodes(G2),nx.number_of_nodes(G3),nx.number_of_nodes(G4)])
edgs= np.array([nx.number_of_edges(G1),nx.number_of_edges(G2),nx.number_of_edges(G3),nx.number_of_edges(G4)])
if valid(cc):
cc=stats.zscore(cc)
else:
cc=np.array([.1,.1,.1,.1])
cc= cc-min(cc)+.1
if valid(spl):
spl=stats.zscore(spl)
else:
spl=np.array([.1,.1,.1,.1])
spl= spl-min(spl)+.1
if valid(nds):
nds=stats.zscore(nds)
else:
nds=np.array([.1,.1,.1,.1])
nds = nds-min(nds)+.1
if valid(edgs):
edgs=stats.zscore(edgs)
else:
edgs=np.array([.1,.1,.1,.1])
edgs=edgs-min(edgs)+.1
r1=(w1*cc[0]+w2*spl[0]+w3*nds[0]+w4*edgs[0])*1000
r2=(w1*cc[1]+w2*spl[1]+w3*nds[1]+w4*edgs[1])*1000
r3=(w1*cc[2]+w2*spl[2]+w3*nds[2]+w4*edgs[2])*1000
r4=(w1*cc[3]+w2*spl[3]+w3*nds[3]+w4*edgs[3])*1000
d={'Player 1:': r1, 'Player 2:': r2,'Player 3:': r3, 'Player 4:': r4}
rank = sorted(d.items(), key=lambda x: x[1], reverse=True)
return ["USAU RANKINGS",str(rank[0][0])+ " " + str(int(rank[0][1])),str(rank[1][0])+" "+ str(int(rank[1][1])),str(rank[2][0])+" "+ str(int(rank[2][1])),str(rank[3][0])+" "+str(int(rank[3][1]))]
except:
return ["Unable to compute rankings! Need data","Player 1","Player 2","Player 3","Player 4"]
def nc_recursive(node, G, ind):
if networkx.number_of_nodes(G) < 3:
n = Node(None)
n.add_child(Node(ind[G.nodes()[0]]))
n.add_child(Node(ind[G.nodes()[1]]))
node.add_child(n)
else:
C = normalized_cut(G)
# print(C)
(G1, G2) = get_subgraphs(G, C)
if networkx.number_of_nodes(G1) > 1:
l = Node(None)
nc_recursive(l, G1, ind)
node.add_child(l)
else:
l = Node(ind[G1.nodes()[0]])
node.add_child(l)
# print(C)
# print("P1 = ")
# print(P1)
# print("P2 = ")
# print(P2)
if networkx.number_of_nodes(G2) > 1:
r = Node(None)
nc_recursive(r, G2, ind)
node.add_child(r)
else:
r = Node(ind[G2.nodes()[0]])
node.add_child(r)
def create_graph_of_agent(agent):
# takes a string as input and outputs a Networkx graph object
(nodes, edges) = parse(agent)
G = nx.MultiDiGraph()
G.add_nodes_from(nodes)
G.add_edges_from(edges)
active_nodes = [1]
next_nodes = []
# print nx.number_of_nodes(G)
for i in range(nx.number_of_nodes(G)):
active_nodes = list(set(active_nodes + next_nodes))
next_nodes = []
for node in active_nodes:
next_nodes += G.successors(node) #0 is predecessors, 1 is successors
#print "next_nodes ",next_nodes
#print active_nodes
#active_nodes=list(set(sorted(active_nodes)))
#print active_nodes
#print nodes
#print edges
extra_nodes = sorted(list(set(range(1, nx.number_of_nodes(G) +1))
- set(active_nodes)), reverse=True)
#print extra_nodes
for i in extra_nodes:
G.remove_node(i)
#print nx.number_of_nodes(G)
return G
def get_my_global_efficiency(filename) :
threshold = 0
f = open(filename[:-4]+'_global_efficiency.dat','w')
g = open(filename[:-4]+'_node_global_efficiency.dat','w')
print f
print g
f.write('threshold\tglob_effic\n')
g.write('node\tthreshold\tnode_glob_effc\n')
for i in range(0,101):
threshold = float(i)/100
G = get_my_threshold_matrix(filename, threshold)
global_efficiency = 0.
for node_i in G :
sum_inverse_dist = 0.
for node_j in G :
if node_i != node_j :
if nx.has_path(G, node_i, node_j) == True :
sum_inverse_dist += 1. / nx.shortest_path_length(G, node_i, node_j)
g.write('%d\t%f\t%f\n' % ((node_i+1), threshold, (sum_inverse_dist / nx.number_of_nodes(G)) )) ##?
global_efficiency += sum_inverse_dist / (nx.number_of_nodes(G) -1.)
g.write("\n")
global_efficiency = global_efficiency / nx.number_of_nodes(G)
f.write("%f\t%f\n" % (threshold, global_efficiency))
f.close()
g.close()
def get_single_network_measures(G, thr):
f = open(out_prfx + 'single_network_measures.dat', 'a')
N = nx.number_of_nodes(G)
L = nx.number_of_edges(G)
D = nx.density(G)
cc = nx.average_clustering(G)
compon = nx.number_connected_components(G)
Con_sub = nx.connected_component_subgraphs(G)
values = []
values_2 =[]
for node in G:
values.append(G.degree(node))
ave_deg = float(sum(values)) / float(N)
f.write("%f\t%d\t%f\t%f\t%f\t%f\t" % (thr, L, D, cc, ave_deg, compon))
#1. threshold, 2. edges, 3. density 4.clustering coefficient
#5. average degree, 6. number of connected components
for i in range(len(Con_sub)):
if nx.number_of_nodes(Con_sub[i])>1:
values_2.append(nx.average_shortest_path_length(Con_sub[i]))
if len(values_2)==0:
f.write("0.\n")
else:
f.write("%f\n" % (sum(values_2)/len(values_2)))
#7. shortest pathway
f.close()
def failure(compagnia):
adiacenzaFinal = numpy.genfromtxt(("/home/protoss/Documenti/Siscomp_datas/data/AdiacenzaEuclidea_{0}.csv".format(compagnia)),delimiter=',',dtype='int')
grafoFinal = networkx.Graph(adiacenzaFinal)
graphSize = networkx.number_of_nodes(grafoFinal)
steps = graphSize
passo = 1
i = 0
ascisse.append(i)
aziendaFinal.append(compagnia)
diametro.append(2)
relSizeGC.append(1)
while (networkx.number_of_nodes(grafoFinal) > passo):
gradiFinal = pandas.DataFrame(grafoFinal.degree().items(), columns=['index', 'grado'])
randomante = gradiFinal['index'].values
randomante = numpy.random.permutation(randomante)
grafoFinal.remove_node(randomante[0])
giantCluster = max(networkx.connected_component_subgraphs(grafoFinal), key = len)
i += 100/steps
ascisse.append(i)
aziendaFinal.append(compagnia)
graphSize = networkx.number_of_nodes(grafoFinal)
diametro.append(networkx.diameter(giantCluster, e=None))
relSizeGC.append((networkx.number_of_nodes(giantCluster))/(float(graphSize)))
def bfs(self, Asmall, start, path=True):
queue = [start]
chain = []
extra_nodes_search = np.ones((nx.number_of_nodes(self.graph),), dtype=np.int)
node_list = -1 * np.ones((nx.number_of_nodes(self.graph),), dtype=np.int)
node_list[start] = start
while queue:
neighbors = np.array(np.nonzero(Asmall[queue[0], :])[1]) # get neighbors as numpy array
for i in range(0, np.size(neighbors)):
if node_list[neighbors[i]] == -1: # Simon's computer dies here
node_list[neighbors[i]] = queue[0]
queue.append(neighbors[i])
qsize = len(queue)
furthest_node = queue[qsize - 1]
queue = queue[1:]
if path:
curr = furthest_node
while curr != start:
chain.append(curr)
extra_nodes_search[curr] = 0
curr = node_list[curr]
chain.append(start)
chain = list(reversed(chain))
extra_nodes_search[start] = 0
return furthest_node, chain
def main():
timeStart = time.time()
if rank == 0:
proc = random.sample(range(1, nx.number_of_nodes(G)), size)
for i in range(1, size):
comm.send(proc[i], dest=i)
starting_node = proc[0]
if check_neighbours(starting_node, None):
print "Graph is hamiltonian! (process", rank, "starting node", starting_node,")"
else:
print "Graph is not hamiltonian (process", rank, "starting node", starting_node,")"
timeEnd = time.time() - timeStart
print timeEnd
comm.Abort()
elif rank < nx.number_of_nodes(G):
starting_node = comm.recv(source=0)
if check_neighbours(starting_node, None):
print "Graph is hamiltonian! (process", rank, "starting node", starting_node,")"
else:
print "Graph is not hamiltonian (process", rank, "starting node", starting_node,")"
timeEnd = time.time() - timeStart
print timeEnd
comm.Abort()
else:
MPI.Finalize()
def reduceGraph(read_g, write_g, minEdgeWeight, minNodeDegree, Lp, Sp):
"""
Simplify the undirected graph and then update the 3 undirected weight properties.
:param read_g: is the graph pickle to read
:param write_g: is the updated graph pickle to write
:param minEdgeWeight: the original weight of each edge should be >= minEdgeWeight
:param minNodeDegree: the degree of each node should be >= minNodeDegree. the degree here is G.degree(node), NOT G.degree(node,weight='weight)
:return: None
"""
G=nx.read_gpickle(read_g)
print 'number of original nodes: ', nx.number_of_nodes(G)
print 'number of original edges: ', nx.number_of_edges(G)
for (u,v,w) in G.edges(data='weight'):
if w < minEdgeWeight:
G.remove_edge(u,v)
for n in G.nodes():
if G.degree(n)<minNodeDegree:
G.remove_node(n)
print 'number of new nodes: ', nx.number_of_nodes(G)
print 'number of new edges: ', nx.number_of_edges(G)
for (a, b, w) in G.edges_iter(data='weight'):
unweight_allocation(G, a, b, w,Lp,Sp)
print 'update weight ok'
nx.write_gpickle(G, write_g)
return
def get_degree_distr(filename):
import networkx as nx
threshold = 0
f = open(filename[:-4]+'_degreedistr.dat','w')
for i in range(0,101):
threshold = float(i)/100
G = get_threshold_matrix(filename, threshold)
check_sum = 0.
degree_hist = {}
for node in G:
if G.degree(node) not in degree_hist:
degree_hist[G.degree(node)] = 1
else:
degree_hist[G.degree(node)] += 1
degrees = range(0, nx.number_of_nodes(G)+1, 1)
keys = degree_hist.keys()
keys.sort()
for item in degrees:
if item in keys:
check_sum += float(degree_hist[item])/float(nx.number_of_nodes(G))
f.write('%d\t%f\t%d\t%f\n' % (item, threshold, degree_hist[item], float(degree_hist[item])/float(nx.number_of_nodes(G))))
#print item, degree_hist[item], float(degree_hist[item])/float(nx.number_of_nodes(G)))
else:
f.write('%d\t%f\t0\t0.\n' % (item, threshold))
f.write("\n")
print 'degree distribution for threshold: %f, check sum: %f' % (threshold, check_sum)
f.close()
def attacco(compagnia):
adiacenzaFinal = numpy.genfromtxt(
("/home/protoss/Documenti/Siscomp_datas/data/AdiacenzaEuclidea_{0}.csv".format(compagnia)),
delimiter=",",
dtype="int",
)
grafoFinal = networkx.Graph(adiacenzaFinal)
graphSize = networkx.number_of_nodes(grafoFinal)
steps = graphSize
passo = 1
i = 0
ascisse.append(i)
aziendaFinal.append(compagnia)
diametro.append(2)
relSizeGC.append(1)
while networkx.number_of_nodes(grafoFinal) > passo:
gradiFinal = pandas.DataFrame(grafoFinal.degree().items(), columns=["index", "grado"])
gradiFinal.sort(["grado"], ascending=[False], inplace=True)
sortedIDnode = gradiFinal["index"].values
grafoFinal.remove_node(sortedIDnode[0])
giantCluster = max(networkx.connected_component_subgraphs(grafoFinal), key=len)
i += 100 / float(steps)
ascisse.append(i)
aziendaFinal.append(compagnia)
newGraphSize = networkx.number_of_nodes(grafoFinal)
# diametro.append(networkx.diameter(giantCluster, e=None))
relSizeGC.append((networkx.number_of_nodes(giantCluster)) / (float(newGraphSize)))
def get_my_degree_distribution(filename) :
threshold = 0
f = open(filename[:-4]+'_degree_distr.dat','w')
print(f)
for i in range(0,101) :
threshold = float(i)/100
G = get_my_threshold_matrix(filename,threshold)
check_sum = 0
degree_hist = {}
for node in G :
if G.degree(node) not in degree_hist :
degree_hist[G.degree(node)] = 1
else :
degree_hist[G.degree(node)] += 1
keys = degree_hist.keys()
keys.sort()
degrees = range(0, nx.number_of_nodes(G)+1 , 1) #?
for item in degrees :
if item in keys :
prob = float(degree_hist[item])/float(nx.number_of_nodes(G))
check_sum += prob
f.write('%d\t%f\t%d\t%f\n'%(item, threshold, degree_hist[item], prob))
else :
f.write('%d\t%f\t0\t0.\n' % (item, threshold))
f.write("\n")
print 'degree distr of threshold: %f, check sum: %f' % (threshold, check_sum)
f.close()
def init():
global projectname
global version_aray
global pos
global x
global y
global size_array
global numframes
global sg
for i in range(6):
data_directory = projectname + "_history/" + projectname + version_array[i] + "/" + projectname
[g, lines] = creategraph.readfile(data_directory)
if i == 0:
sg = creategraph.refine(g, 45)
[pos, x, y] = creategraph.coordinate(sg)
size = creategraph.point_sizes(sg, lines)
zeros = np.array([0] * len(size))
print 'len(size) = ', len(size)
print 'zeros = ', zeros
size_array.append(zeros)
size_array.append(size)
else:
# create the graph induced by nodes from sg
subg = nx.subgraph(g, nx.nodes(sg))
print subg, sg
if nx.number_of_nodes(subg) != nx.number_of_nodes(sg):
print 'panic at 34'
else: # v this seems to be a error, but not
size = creategraph.point_sizes(sg, lines)
size_array.append(size)
x = np.array(x)
y = np.array(y)
size_array = np.array(size_array)
def run_main():
file = str(sys.argv[1])
f = open(file, 'r')
print "\nReading inputfile:", file, "..."
edgelist = []
for line in f.readlines():
edgelist.append((int(line.split()[0]), int(line.split()[1])))
Directed_G = nx.DiGraph(edgelist)
Undirected_G = Directed_G.to_undirected()
#plt.figure(figsize=(8,8))
#nx.draw(Directed_G,pos=nx.spring_layout(Directed_G))
#plt.draw()
#time.sleep(0.1)
# compute other things
print "Number of nodes involved in network:", nx.number_of_nodes(Undirected_G)
print "Number of edges:", nx.number_of_edges(Undirected_G)
print "Average degree:", nx.number_of_edges(Undirected_G) / float(nx.number_of_nodes(Undirected_G))
t0 = time.clock()
print "Average clustering coefficient:", compute_clustering_coefficient(Directed_G, Undirected_G)
print "Took:", time.clock() - t0, "seconds"
t1 = time.clock()
print "Average path length:", average_shortest_path(Directed_G, Undirected_G)
print "Took:", time.clock() - t1, "seconds"
print "Total time:", time.clock() - t0, "seconds"
report_final_stats()
counter += 1
second_counter += 1
def _draw(
self,
layout='spring',
n_color='blue',
n_size=15,
n_alpha=0.5,
e_alpha=0.1,
e_arrows=False,
scale_by_degree=False):
"""
Draw topology using the draw() command from networkx.
USAGE: topology.draw(layout = "spring", n_size = 15, scale_by_degree = False, n_color = 'blue', n_alpha = 0.5, e_alpha = 0.1, e_arrows=False)
* layout: Network layout. Can be 'spring' or 'circular'.
* n_size: The size of nodes. Becomes scaling factor when scale_by_degree=True.
* scale_by_degree: When True, nodes will be sized proportional to their degree.
* n_color: Node color.
* n_alpha: Transparency of nodes. Takes value between 0 and 1.
* e_elpha: Transparency of edges. Takes value between 0 and 1.
* e_arrows: Plots arrows on the edges for directed graphs
* scale_by_degree: When True, nodes will be sized proportional to their degree.
"""
try:
import networkx as nx
except ImportError:
raise ImportError('Could not import the networkx module.')
try:
import matplotlib.pyplot as pl
except ImportError:
raise ImportError('Could not improt the MatPlotLib module.')
if self.number_of_vertices == 0 or self.number_of_vertices == 1:
raise ValueError(
'Cannot draw topology with one single vertex or less.')
G = self.to_networkx()
node_sizes = list(range(nx.number_of_nodes(G)))
for i in range(nx.number_of_nodes(G)):
if scale_by_degree:
node_sizes[i] = nx.degree(G, i) * n_size
else:
node_sizes[i] = n_size
if layout == "spring":
pos = nx.spring_layout(self.to_networkx())
if layout == "circular":
pos = nx.circular_layout(self.to_networkx())
pl.figure()
nx.draw_networkx_edges(
self.to_networkx(), pos, alpha=e_alpha, arrows=e_arrows)
nx.draw_networkx_nodes(
self.to_networkx(),
pos,
node_size=node_sizes,
node_color=n_color,
alpha=n_alpha)
pl.axis('off')
pl.show()
def test_strong_product_size():
K5=nx.complete_graph(5)
P5=nx.path_graph(5)
K3 = nx.complete_graph(3)
G=strong_product(P5,K3)
assert_equal(nx.number_of_nodes(G),5*3)
G=strong_product(K3,K5)
assert_equal(nx.number_of_nodes(G),3*5)
def test_tensor_product_size():
P5 = nx.path_graph(5)
K3 = nx.complete_graph(3)
K5 = nx.complete_graph(5)
G = nx.tensor_product(P5, K3)
assert_equal(nx.number_of_nodes(G), 5 * 3)
G = nx.tensor_product(K3, K5)
assert_equal(nx.number_of_nodes(G), 3 * 5)
def find_largest_component(self):
G = self.graph
list_Graphs = nx.weakly_connected_component_subgraphs(G)
max_component = list_Graphs[0]
for g in list_Graphs:
if nx.number_of_nodes(g) > nx.number_of_nodes(max_component):
max_component = g
return max_component
def test_strong_product():
null=nx.null_graph()
empty1=nx.empty_graph(1)
empty10=nx.empty_graph(10)
K2=nx.complete_graph(2)
K3=nx.complete_graph(3)
K5=nx.complete_graph(5)
K10=nx.complete_graph(10)
P2=nx.path_graph(2)
P3=nx.path_graph(3)
P5=nx.path_graph(5)
P10=nx.path_graph(10)
# null graph
G=strong_product(null,null)
assert_true(nx.is_isomorphic(G,null))
# null_graph X anything = null_graph and v.v.
G=strong_product(null,empty10)
assert_true(nx.is_isomorphic(G,null))
G=strong_product(null,K3)
assert_true(nx.is_isomorphic(G,null))
G=strong_product(null,K10)
assert_true(nx.is_isomorphic(G,null))
G=strong_product(null,P3)
assert_true(nx.is_isomorphic(G,null))
G=strong_product(null,P10)
assert_true(nx.is_isomorphic(G,null))
G=strong_product(empty10,null)
assert_true(nx.is_isomorphic(G,null))
G=strong_product(K3,null)
assert_true(nx.is_isomorphic(G,null))
G=strong_product(K10,null)
assert_true(nx.is_isomorphic(G,null))
G=strong_product(P3,null)
assert_true(nx.is_isomorphic(G,null))
G=strong_product(P10,null)
assert_true(nx.is_isomorphic(G,null))
G=strong_product(P5,K3)
assert_equal(nx.number_of_nodes(G),5*3)
G=strong_product(K3,K5)
assert_equal(nx.number_of_nodes(G),3*5)
#No classic easily found classic results for strong product
G = nx.erdos_renyi_graph(10,2/10.)
H = nx.erdos_renyi_graph(10,2/10.)
GH = strong_product(G,H)
for (u_G,u_H) in GH.nodes_iter():
for (v_G,v_H) in GH.nodes_iter():
if (u_G==v_G and H.has_edge(u_H,v_H)) or \
(u_H==v_H and G.has_edge(u_G,v_G)) or \
(G.has_edge(u_G,v_G) and H.has_edge(u_H,v_H)):
assert_true(GH.has_edge((u_G,u_H),(v_G,v_H)))
else:
assert_true(not GH.has_edge((u_G,u_H),(v_G,v_H)))
def validate_constituency_parse(tokenization):
"""
Args:
tokenization (concrete.structure.ttypes.Tokenization)
Returns:
bool: True if tokenization's constituency parse is valid, False otherwise
"""
valid = True
if tokenization.parse:
total_constituents = len(tokenization.parse.constituentList)
logging.debug(ilm(6, "tokenization '%s' has %d constituents" % (tokenization.uuid, total_constituents)))
total_uuid_mismatches = 0
constituent_id_set = set()
constituent_parse_tree = nx.DiGraph()
for constituent in tokenization.parse.constituentList:
# Add nodes to parse tree
constituent_parse_tree.add_node(constituent.id)
if constituent.id not in constituent_id_set:
constituent_id_set.add(constituent.id)
else:
valid = False
logging.error(ilm(7, "constituent ID %d has already been used in this sentence's tokenization" % constituent.id))
# Per the Concrete 'structure.thrift' file, tokenSequence may not be defined:
# "Typically, this field will only be defined for leaf constituents (i.e., constituents with no children)."
if constituent.tokenSequence and constituent.tokenSequence.tokenizationId != tokenization.uuid:
total_uuid_mismatches += 1
if total_uuid_mismatches > 0:
valid = False
logging.error(ilm(6, "tokenization '%s' has UUID mismatch for %d/%d constituents" %
(tokenization.uuid, total_uuid_mismatches, total_constituents)))
# Add edges to constituent parse tree
for constituent in tokenization.parse.constituentList:
if constituent.childList:
for child_id in constituent.childList:
constituent_parse_tree.add_edge(constituent.id, child_id)
# Check if constituent parse "tree" is actually a tree
undirected_graph = constituent_parse_tree.to_undirected()
if not nx.is_connected(undirected_graph):
valid = False
logging.error(ilm(6, "The constituent parse \"tree\" is not a fully connected graph - the graph has %d components" %
len(nx.connected_components(undirected_graph))))
if nx.number_of_nodes(constituent_parse_tree) != nx.number_of_edges(constituent_parse_tree) + 1:
valid = False
logging.error(ilm(6, "The constituent parse \"tree\" is not a tree. |V| != |E|+1 (|V|=%d, |E|=%d)" %
(nx.number_of_nodes(constituent_parse_tree), nx.number_of_edges(constituent_parse_tree))))
return valid
def redirect(graph, component):
current_nodes = graph.nodes()
retained_nodes = component.nodes()
out.write('\n' + 'There are %d nodes in retained_nodes' %len(retained_nodes))
for entry in current_nodes:
if not entry in retained_nodes:
graph.remove_node(entry)
out.write('\n' + 'There are %d nodes in component' %nx.number_of_nodes(component))
out.write('\n' + 'There are %d nodes in graph' %nx.number_of_nodes(graph))
return graph
def one_d_search(G, F, k, ind):
"""
Cut computation. Perform 1-D search for beta using golden search.
Input:
* G: graph
* F: graph signal
* k: max edges to be cut
* n: number of chebyshev polynomials
* ind: vertex index vertex: unique integer
Output:
* cut
"""
C = laplacian_complete(networkx.number_of_nodes(G))
A = weighted_adjacency_complete(G,F, ind)
CAC = numpy.dot(numpy.dot(C,A), C)
start = numpy.ones(networkx.number_of_nodes(G))
L = networkx.laplacian_matrix(G).todense()
#Upper and lower bounds for search
a = 0.
b = 1000.
c=b-gr*(b-a)
d=a+gr*(b-a)
#Tolerance
tol = 1.
resab = {}
resab["size"] = k + 1
#golden search
while abs(c-d)>tol or resab["size"] > k:
resc = spectral_cut(CAC, L, C, A, start, F, G, c, k, ind)
resd = spectral_cut(CAC, L, C, A, start, F, G, d, k, ind)
if resc["size"] <= k:
if resc["score"] > resd["score"]:
start = numpy.array(resc["x"])
b = d
d = c
c=b-gr*(b-a)
else:
start = numpy.array(resd["x"])
a=c
c=d
d=a+gr*(b-a)
else:
start = numpy.array(resc["x"])
a=c
c=d
d=a+gr*(b-a)
resab = spectral_cut(CAC, L, C, A, start, F, G, (b+a) / 2, k, ind)
return resab
def get_small_worldness(filename):
import networkx as nx
threshold = 0
f = open(filename[:-4]+'_small_worldness.dat','w')
for i in range(0,101):
threshold = float(i)/100
G = get_threshold_matrix(filename, threshold)
ER_graph = nx.erdos_renyi_graph(nx.number_of_nodes(G), nx.density(G))
cluster = nx.average_clustering(G)
ER_cluster = nx.average_clustering(ER_graph)
transi = nx.transitivity(G)
ER_transi = nx.transitivity(ER_graph)
print 'threshold: %f, average cluster coefficient: %f, random nw: %f, transitivity: %f, random nw: %f' %(threshold, cluster, ER_cluster, transi, ER_transi)
f.write("%f\t%f\t%f" % (threshold, cluster, ER_cluster))
components = nx.connected_component_subgraphs(G)
ER_components = nx.connected_component_subgraphs(ER_graph)
values = []
ER_values = []
for i in range(len(components)):
if nx.number_of_nodes(components[i]) > 1:
values.append(nx.average_shortest_path_length(components[i]))
for i in range(len(ER_components)):
if nx.number_of_nodes(ER_components[i]) > 1:
ER_values.append(nx.average_shortest_path_length(ER_components[i]))
if len(values) == 0:
f.write("\t0.")
else:
f.write("\t%f" % (sum(values)/len(values)))
if len(ER_values) == 0:
f.write("\t0.")
else:
f.write("\t%f" % (sum(ER_values)/len(ER_values)))
f.write("\t%f\t%f" % (transi, ER_transi))
if (ER_cluster*sum(values)*len(values)*sum(ER_values)*len(ER_values)) >0 :
S_WS = (cluster/ER_cluster) / ((sum(values)/len(values)) / (sum(ER_values)/len(ER_values)))
else:
S_WS = 0.
if (ER_transi*sum(values)*len(values)*sum(ER_values)*len(ER_values)) >0 :
S_Delta = (transi/ER_transi) / ((sum(values)/len(values)) / (sum(ER_values)/len(ER_values)))
else:
S_Delta = 0.
f.write("\t%f\t%f" % (S_WS, S_Delta))
f.write("\n")
f.close()
print "1:threshold 2:cluster-coefficient 3:random-cluster-coefficient 4:shortest-pathlength 5:random-shortest-pathlength 6:transitivity 7:random-transitivity 8:S-Watts-Strogatz 9:S-transitivity"
def test_strong_product_combinations():
P5 = nx.path_graph(5)
K3 = nx.complete_graph(3)
G = strong_product(P5, K3)
assert_equal(nx.number_of_nodes(G), 5 * 3)
G = strong_product(nx.MultiGraph(P5), K3)
assert_equal(nx.number_of_nodes(G), 5 * 3)
G = strong_product(P5, nx.MultiGraph(K3))
assert_equal(nx.number_of_nodes(G), 5 * 3)
G = strong_product(nx.MultiGraph(P5), nx.MultiGraph(K3))
assert_equal(nx.number_of_nodes(G), 5 * 3)
def test_number_of_nodes(self):
assert self.G.number_of_nodes() == nx.number_of_nodes(self.G)
assert self.DG.number_of_nodes() == nx.number_of_nodes(self.DG)
test_name = '-'.join([opt['g_type'], opt['test_min_n'], opt['test_max_n']])
result_file = '%s/test-%s-gnn-%s-%s.csv' % (opt['save_dir'], test_name,
opt['min_n'], opt['max_n'])
n_test = 1000
frac = 0.0
with open(result_file, 'w') as f_out:
print 'testing'
sys.stdout.flush()
idx = 0
prepared_train_data = []
train_opt_tours = []
for g in tqdm(TestSet()):
api.InsertGraph(g, is_test=True)
t1 = time.time()
val, sol = api.GetSol(idx, nx.number_of_nodes(g))
prepared_train_data.append((g, val))
train_opt_tours.append(sol[1:])
t2 = time.time()
f_out.write('%.8f,' % val)
f_out.write('%d' % sol[0])
#print "Num of nodes in graph: ",nx.number_of_nodes(g)," solution len: ",sol[0]
if nx.number_of_nodes(g) != sol[0]:
print "Problem"
for i in range(sol[0]):
f_out.write(' %d' % sol[i + 1])
f_out.write(',%.6f\n' % (t2 - t1))
frac += val
idx += 1
with open("prepared_train_data.pkl", 'wb') as f:
#roadSegGraph = nx.read_graphml("roadSegGraph_110.graphml")
#roadSegGraph = nx.read_graphml("roadSegGraph.graphml")
nLayers = 4
largeNumber = 10**5
filename = 'example_all_routes.dat'
#number of routes
num_routes = nsamples
pathNumber = 1
data, data_var = init_dataFile(nLayers, budget, num_routes, largeNumber)
rand_routes = random_routes(roadSegGraph)
non_subroute = {}
nnodes = nx.number_of_nodes(roadSegGraph)
node_list = roadSegGraph.nodes()
node_indx = {}
count = 0
for i in node_list:
node_indx[i] = count
count += 1
for u in range(1, nnodes * nnodes + 1):
non_subroute[u] = True
for i, j in rand_routes:
if i != j:
shortest_path = nx.shortest_path(roadSegGraph,
source=i,
target=j,
import sys
import networkx as nx
import random as rand
from numpy import random
from collections import deque
g = nx.read_edgelist('cambridge_net.txt', create_using=nx.DiGraph(), nodetype=int)
total_nodes = nx.number_of_nodes(g)
target_nodes = 0.15 * total_nodes
print "Total Nodes:", total_nodes
print "Target Nodes:", target_nodes
print nx.info(g)
to_burn = deque()
finished = False
p = 2.0 / 3.0
print p
while not(finished):
to_burn.clear()
to_burn.append(rand.choice(g.nodes()))
while to_burn:
burning = to_burn.popleft()
nodes = g.neighbors(burning)
g.remove_node(burning)
def hierarchical_clustering(G, resolution=1):
for n in G.nodes_iter():
G.node[n]['ancIdxs'] = []
# A dendrogram is a tree and each level is a partition of the graph nodes
dendo = community.generate_dendrogram(G, resolution=resolution)
num_levels = len(dendo)
clusters_per_level = []
for level in range(num_levels):
partition = community.partition_at_level(dendo, level)
clusters = list(set(partition.values()))
clusters_per_level.append(clusters)
print('clusters at level', level, 'are', clusters)
for n, c in partition.items():
G.node[n]['ancIdxs'].append(c)
num_nodes = nx.number_of_nodes(G)
def get_cluster_idx(level, idx):
offset = num_nodes
for i in range(level):
offset += len(clusters_per_level[i])
return offset + idx
cluster_list = []
for n in G.nodes_iter():
node = G.node[n]
node_clusters = node['ancIdxs']
for level in range(len(node['ancIdxs'])):
node_clusters[level] = get_cluster_idx(level, node_clusters[level])
cluster_list.append({
'idx': n,
'nodeIdx': n,
'parentIdx': node_clusters[0],
'height': 0
})
for level, clusters in enumerate(clusters_per_level):
for c in clusters:
cluster_list.append({
'idx': get_cluster_idx(level, c),
'height': level + 1
})
for n in G.nodes_iter():
node = G.node[n]
node_clusters = node['ancIdxs']
for level in range(len(node_clusters) - 1):
cluster_list[node_clusters[level]]['parentIdx'] = node_clusters[
level + 1]
# Root
root_cluster_idx = len(cluster_list)
cluster_list.append({
'idx': root_cluster_idx,
'height': len(clusters_per_level) + 1
})
for c in clusters_per_level[-1]:
cluster_list[get_cluster_idx(num_levels - 1,
c)]['parentIdx'] = root_cluster_idx
return cluster_list
return UU
def iso(G1, glist):
"""Quick and dirty nonisomorphism checker used to check isomorphisms."""
for G2 in glist:
if isomorphic(G1, G2):
return True
return False
if __name__ == '__main__':
G = atlas6()
print("graph has %d nodes with %d edges"\
%(nx.number_of_nodes(G), nx.number_of_edges(G)))
print(nx.number_connected_components(G), "connected components")
try:
import pygraphviz
from networkx.drawing.nx_agraph import graphviz_layout
except ImportError:
try:
import pydot
from networkx.drawing.nx_pydot import graphviz_layout
except ImportError:
raise ImportError("This example needs Graphviz and either "
"PyGraphviz or pydot")
import matplotlib.pyplot as plt
plt.figure(1, figsize=(8, 8))
"""
run考虑到7月9日、10日的跌停股票=1或0，这两天考察停牌股票，要单独运行（先把这两天的文件挪到另一个文件夹）
"""
w_threshold = 0.95
os.chdir('/Users/shine/work_hard/financial_network/data/threshold/stock_alpha')
V = read_edgelist('2015-1.edgelist')
w = weight(V)
w = pd.DataFrame(w)
beta = w.quantile(q = w_threshold)
print beta
V = remove_edges(V, beta)
print "after remove edges, stock network nodes and edges are ",nx.number_of_nodes(V), nx.number_of_edges(V)
lower = []
sus = []
sus_lower = []
os.chdir('/Users/shine/work_hard/financial_network/data/status_wind_612_710')
FileList = glob.glob('*.xlsx') #for workingfile in filelist
print FileList
print "FileList length is", len(FileList) #共20个文件
for workingfile in FileList:
print workingfile,'is working now'
filename = workingfile.split('.')[0]
print 'filename is', filename
os.chdir('/Users/shine/work_hard/financial_network/data/status_wind_612_710')
status = pd.ExcelFile(workingfile)
status = pd.read_excel(status, 'Wind资讯'.decode('utf-8'))
""" Test Fragmentation index with data from Borgatti """ import networkx as nx # # # 2 complete graphs a 5 nodes A = nx.to_undirected(nx.complete_graph([1,2,3,4,5])) B = nx.to_undirected(nx.complete_graph([6,7,8,9,10])) # A=nx.to_undirected(A) # B=nx.to_undirected(B) c = nx.union(A,B) ch = nx.harmonic_centrality(c) print(ch) print(sum(ch.values())/(nx.number_of_nodes(c)*(nx.number_of_nodes(c)-1))) print(1. - (sum(ch.values())/(nx.number_of_nodes(c)*(nx.number_of_nodes(c)-1)))) # # # 2 path 1->2->3->4->5 and 6->7->8->9->10 a = nx.to_undirected(nx.path_graph([1,2,3,4,5])) b = nx.to_undirected(nx.path_graph([6,7,8,9,10])) c=nx.union(a,b) ch = nx.harmonic_centrality(c) print(ch) print(sum(ch.values())/(nx.number_of_nodes(c)*(nx.number_of_nodes(c)-1))) print(1. - (sum(ch.values())/(nx.number_of_nodes(c)*(nx.number_of_nodes(c)-1))))
def populate_instance_and_run_as_util(self, g, h): #, lsape_instance):
"""
/*!
* @brief Runs the method with options specified by set_options() and provides access to constructed LSAPE instance.
* @param[in] g Input graph.
* @param[in] h Input graph.
* @param[out] result Result variable.
* @param[out] lsape_instance LSAPE instance.
*/
"""
result = {'node_maps': [], 'lower_bound': 0, 'upper_bound': np.inf}
# Populate the LSAPE instance and set up the solver.
nb1, nb2 = nx.number_of_nodes(g), nx.number_of_nodes(h)
lsape_instance = np.ones((nb1 + nb2, nb1 + nb2)) * np.inf
# lsape_instance = np.empty((nx.number_of_nodes(g) + 1, nx.number_of_nodes(h) + 1))
self.populate_instance(g, h, lsape_instance)
# nb1, nb2 = nx.number_of_nodes(g), nx.number_of_nodes(h)
# lsape_instance_new = np.empty((nb1 + nb2, nb1 + nb2)) * np.inf
# lsape_instance_new[nb1:, nb2:] = 0
# lsape_instance_new[0:nb1, 0:nb2] = lsape_instance[0:nb1, 0:nb2]
# for i in range(nb1): # all u's neighbor
# lsape_instance_new[i, nb2 + i] = lsape_instance[i, nb2]
# for i in range(nb2): # all u's neighbor
# lsape_instance_new[nb1 + i, i] = lsape_instance[nb2, i]
# lsape_solver = LSAPESolver(lsape_instance_new)
lsape_solver = LSAPESolver(lsape_instance)
# Solve the LSAPE instance.
if self._solve_optimally:
lsape_solver.set_model(self._lsape_model)
else:
lsape_solver.set_greedy_method(self._greedy_method)
lsape_solver.solve(self._max_num_solutions)
# Compute and store lower and upper bound.
if self._compute_lower_bound and self._solve_optimally:
result['lower_bound'] = lsape_solver.minimal_cost(
) * self._lsape_lower_bound_scaling_factor(g, h) # @todo: test
for solution_id in range(0, lsape_solver.num_solutions()):
result['node_maps'].append(
NodeMap(nx.number_of_nodes(g), nx.number_of_nodes(h)))
misc.construct_node_map_from_solver(lsape_solver,
result['node_maps'][-1],
solution_id)
self._ged_data.compute_induced_cost(g, h, result['node_maps'][-1])
# Add centralities and reoptimize.
if self._centrality_weight > 0 and self._centrality_method != 'NODE':
print('This is not implemented.')
pass # @todo
# Sort the node maps and set the upper bound.
if len(result['node_maps']) > 1 or len(
result['node_maps']) > self._max_num_solutions:
print('This is not implemented.') # @todo:
pass
if len(result['node_maps']) == 0:
result['upper_bound'] = np.inf
else:
result['upper_bound'] = result['node_maps'][0].induced_cost()
return result
import sys
import networkx as nx
import random as rand
from numpy import random
print "Reading Graph"
sys.stdout.flush()
g = nx.read_edgelist('cambridge_net.txt',
create_using=nx.Graph(),
nodetype=int)
target_nodes = 0.15 * nx.number_of_nodes(g)
print "Target Nodes:", target_nodes
sys.stdout.flush()
print "Attempting with limit of len(g)"
sys.stdout.flush()
g_sample = nx.Graph()
p = 0.15
steps = 0
limit = nx.number_of_nodes(g)
finished = False
while not (finished):
source = rand.choice(g.nodes())
node = source
steps = 0
while (steps < limit):
steps = steps + 1
words = set()
for line in fh.readlines():
line = line.decode()
if line.startswith('*'):
continue
w = str(line[0:5])
words.add(w)
return generate_graph(words)
if __name__ == '__main__':
G = words_graph()
print("Loaded words_dat.txt containing 5757 five-letter English words.")
print("Two words are connected if they differ in one letter.")
print("Graph has %d nodes with %d edges" %
(nx.number_of_nodes(G), nx.number_of_edges(G)))
print("%d connected components" % nx.number_connected_components(G))
for (source, target) in [
('chaos', 'order'),
('nodes', 'graph'),
('moron', 'smart'),
('flies', 'swims'),
('mango', 'peach'),
('pound', 'marks'),
]:
print("Shortest path between %s and %s is" % (source, target))
try:
sp = nx.shortest_path(G, source, target)
for n in sp:
print(n)
N = 1000
k = 190
disease_largest_cc = 35
second_largest = 14
fh = open(os.path.join('./PPI_Graphs', 'reactomefi2015.tsv'), 'rb')
G = nx.read_edgelist(fh, delimiter='\t')
nodes_all = G.nodes()
for i in range(0, N):
genes = []
genes = random.sample(nodes_all, k)
shortest_length = sys.maxint
H = G.subgraph(genes)
nodes = nx.number_of_nodes(H)
if (nodes != 0):
#Get All Components
Gcc = sorted(nx.connected_component_subgraphs(H),
key=len,
reverse=True)
first = Gcc[0]
second = Gcc[1]
G0 = nx.number_of_nodes(first)
G1 = nx.number_of_nodes(second)
#Diameter
c1_diam.append(nx.diameter(first))
if nx.diameter(first) > 12:
c1d += 1
except IndexError:
sys.exit(
"Motif graph properties\npython %s <tsv file generated by edges2table.pl> similarity_cutoff zscore_cutoff"
% (sys.argv[0]))
opr_nodes = operon_nodes(crs_f, similarity_cutoff, zscore_cutoff)
#pprint(opr_nodes)
G = motif_graph(crs_f, similarity_cutoff, zscore_cutoff)
loo_f = "../../data/LOO_per_matrix_site.tsv"
regulon_f = "../../data/regulon_by_first_gene.txt"
LOO = read_LOO(loo_f)
regulon = read_regulon(regulon_f)
print("#graph has %d nodes with %d edges, edges to nodes ratio: %f, edge average zscore: %f, %f transitivity"\
%(nx.number_of_nodes(G),
nx.number_of_edges(G),
edge_density(nx.number_of_edges(G), nx.number_of_nodes(G)),
edge_average_zscore(G),
nx.transitivity(G)))
print("#", nx.number_connected_components(G), "connected components")
print(
"reg\tLOO\tsize\tnodes\tedges\tratio\tavg_zscore\tnumber_connected_components\tlargest_comp_size\ttransitivity"
)
for reg in regulon.keys():
if len(regulon[reg]) > 2:
nodes = list()
for gi in regulon[reg]:
for n in opr_nodes[gi]:
import networkx as nx
import random
from bokeh.io import show, curdoc
from bokeh.layouts import Column
from bokeh.models import Plot, Range1d, MultiLine, Circle
from bokeh.models.graphs import from_networkx
from bokeh.models.widgets import Button
from bokeh.palettes import Spectral4, Spectral10
L = 78
G=nx.karate_club_graph()
N = nx.number_of_nodes(G)
plot = Plot(plot_width=400, plot_height=400,
x_range=Range1d(-1.1,1.1), y_range=Range1d(-1.1,1.1))
graph_renderer = from_networkx(G, nx.circular_layout, scale=1, center=(0,0))
graph_renderer.node_renderer.glyph = Circle(size=15, fill_color="colors", line_width=1.0)
graph_renderer.node_renderer.data_source.data["colors"] = [Spectral4[0] ]* N
graph_renderer.edge_renderer.glyph = MultiLine(line_color="colors", line_alpha=0.8, line_width="widths")
graph_renderer.edge_renderer.data_source.data["colors"] = ["black"] * L
graph_renderer.edge_renderer.data_source.data["widths"] = [2] * L
plot.renderers.append(graph_renderer)
def calc(G, invariant):
if invariant == 'domination_number':
return domination_number(G)
elif invariant == 'chromatic_number':
return chromatic_number(G)
elif invariant == 'total_domination_number':
return total_domination_number(G)
elif invariant == 'connected_domination_number':
return GPY.connected_domination_number(G)
elif invariant == 'independent_domination_number':
return independent_domination_number(G)
elif invariant == 'slater':
return GPY.slater(G)
elif invariant == 'sub_total_domination_number':
return GPY.sub_total_domination_number(G)
elif invariant == 'annihilation_number':
return GPY.annihilation_number(G)
elif invariant == 'independence_number':
return independence_number(G)
elif invariant == 'power_domination_number':
return GPY.power_domination_number(G)
elif invariant == 'residue':
return GPY.residue(G)
elif invariant == 'k_residual_index':
return GPY.k_residual_index(G)
elif invariant == 'connected_zero_forcing_number':
return GPY.connected_zero_forcing_number(G)
elif invariant == 'total_zero_forcing_number':
return GPY.total_zero_forcing_number(G)
elif invariant == 'zero_forcing_number':
return GPY.zero_forcing_number(G)
elif invariant == 'diameter':
return nx.diameter(G)
elif invariant == 'radius':
return nx.radius(G)
elif invariant == 'order':
return nx.number_of_nodes(G)
elif invariant == 'size':
return nx.number_of_edges(G)
elif invariant == 'min_degree':
return GPY.min_degree(G)
elif invariant == 'max_degree':
return GPY.max_degree(G)
elif invariant == 'number_of_min_degree_nodes':
return GPY.number_of_min_degree_nodes(G)
elif invariant == 'number_of_degree_one_nodes':
return GPY.number_of_degree_one_nodes(G)
elif invariant == 'number_of_max_degree_nodes':
return GPY.number_of_max_degree_nodes(G)
elif invariant == 'clique_number':
return GPY.clique_number(G)
elif invariant == 'min_maximal_matching_number':
return GPY.min_maximal_matching_number(G)
elif invariant == 'matching_number':
return matching_number(G)
elif invariant == 'triameter':
return triameter(G)
elif invariant == 'vertex_cover_number':
return vertex_cover_number(G)
elif invariant == 'randic_index':
return randic_index(G)
elif invariant == 'augmented_randic_index':
return augmented_randic_index(G)
elif invariant == 'harmonic_index':
return harmonic_index(G)
elif invariant == 'atom_bond_connectivity_index':
return atom_bond_connectivity_index(G)
elif invariant == 'sum_connectivity_index':
return sum_connectivity_index(G)
else:
print('ERROR')
return False
if len(result[key]) != 0:
for friend_id in result[key]:
Network.add_node(friend_id)
for key in result:
if len(result[key]) != 0:
for friend_id in result[key]:
Network.add_edge(key, friend_id)
# draw the network, store the graph as png file and show the graph
nx.draw(Network)
plt.savefig("Network.png")
plt.show()
# print the number of nodes and edges, diameter and average distance of the network
print("The number of nodes is: ")
print(nx.number_of_nodes(Network))
print("The number of edges is: ")
print(nx.number_of_edges(Network))
print("The diameter is: ")
print(nx.diameter(Network))
print("The average distance of the graph is: ")
print(nx.average_shortest_path_length(Network))
# this txt file is used to store the number of nodes and edges, diameter and average distance information
f1 = open("stats.txt", "w")
f1.write("The number of nodes is: ")
f1.write(str(nx.number_of_nodes(Network)))
f1.write("\nThe number of edges is: ")
f1.write(str(nx.number_of_edges(Network)))
f1.write("\nThe diameter is: ")
f1.write(str(nx.diameter(Network)))
def show_info(self):
for node, attr in dict(self.bw.nodes).items():
print('{0} -> {1}'.format(node, attr))
print('Number of Nodes: {0}'.format(nx.number_of_nodes(self.bw)))
print('Number of Edges: {0}'.format(nx.number_of_edges(self.bw)))
for c, v in p.items():
if (v != ville):
G.remove_node(c)
# second clustering to determine group inside a city
p = community.best_partition(G, weight='weight')
for c, v in p.items():
color_map.append(v)
# export clusters for the city
f = open("data/cluster_" + str(ville) + ".csv", "w")
for c, v in p.items():
f.write(str(c) + ";" + str(v) + ";" + str(ville) + "\n")
f.close()
# export gexf format for gephi
# nx.write_gexf(G, "graph.gexf", prettyprint=True)
print ("Number of edges : ", i)
print ("Number of nodes : ", nx.number_of_nodes(G))
weights = [G[u][v]['weight']/7 for u,v in G.edges] # take weight into account for edges
# draw and show
nx.draw(G, node_size=50, width=weights, node_color=color_map)
plt.show()
def weight(c):
return float(2 * nx.number_of_edges(c) / nx.number_of_nodes(c))
def _commonwalkkernel_exp(g1, g2, node_label, edge_label, beta):
"""Calculate walk graph kernels up to n between 2 graphs using exponential
series.
Parameters
----------
Gn : List of NetworkX graph
List of graphs between which the kernels are calculated.
node_label : string
Node attribute used as label.
edge_label : string
Edge attribute used as label.
beta : integer
Weight.
ij : tuple of integer
Index of graphs between which the kernel is computed.
Return
------
kernel : float
The common walk Kernel between 2 graphs.
"""
# get tensor product / direct product
gp = direct_product(g1, g2, node_label, edge_label)
# return 0 if the direct product graph have no more than 1 node.
if nx.number_of_nodes(gp) < 2:
return 0
A = nx.adjacency_matrix(gp).todense()
# print(A)
# from matplotlib import pyplot as plt
# nx.draw_networkx(G1)
# plt.show()
# nx.draw_networkx(G2)
# plt.show()
# nx.draw_networkx(gp)
# plt.show()
# print(G1.nodes(data=True))
# print(G2.nodes(data=True))
# print(gp.nodes(data=True))
# print(gp.edges(data=True))
ew, ev = np.linalg.eig(A)
# print('ew: ', ew)
# print(ev)
# T = np.matrix(ev)
# print('T: ', T)
# T = ev.I
D = np.zeros((len(ew), len(ew)))
for i in range(len(ew)):
D[i][i] = np.exp(beta * ew[i])
# print('D: ', D)
# print('hshs: ', T.I * D * T)
# print(np.exp(-2))
# print(D)
# print(np.exp(weight * D))
# print(ev)
# print(np.linalg.inv(ev))
exp_D = ev * D * ev.T
# print(exp_D)
# print(np.exp(weight * A))
# print('-------')
return exp_D.sum()
def fit_spearmans(mass_boxes, range):
ln_box_number = np.log(mass_boxes)
ln_range = np.log(range)
gradient = stats.spearmanr(ln_range, ln_box_number)[0] * (
np.std(ln_box_number) / np.std(ln_range))
constant = ln_box_number[0] - gradient * ln_range[0]
return (gradient, constant)
if __name__ == "__main__":
graph = graph_magic.get_graph_from_file(
"../../BIOGRID-ORGANISM-3.5.165.tab2/BIOGRID-ORGANISM-Human_Immunodeficiency_Virus_1-3.5.165.tab2.txt"
)
# graph = graph_magic.get_graph_from_file(
# "../../BIOGRID-ORGANISM-3.5.165.tab2/BIOGRID-ORGANISM-Escherichia_coli_K12_MC4100_BW2952-3.5.165.tab2.txt")
node_number = nx.number_of_nodes(graph)
print(node_number)
mass_boxes = fd.compact_box_burning(graph)
print(mass_boxes)
# plt.subplot(121)
# nx.draw(graph, with_labels=True, font_weight='bold')
# plt.show()
max_length = len(mass_boxes)
lengths = np.arange(1, max_length + 1)
(spearman_gradient, spearman_constant) = fit_spearmans(mass_boxes, lengths)
(pearson_gradient, pearson_constant) = np.polyfit(np.log(lengths),
np.log(mass_boxes),
deg=1)
boxes_spearman_theory = list(
map(box_theory, lengths, [spearman_gradient] * max_length,
[node_number] * max_length))
print(nx.is_connected(g))
# next, see how many pieces the graph is actually in
print(nx.number_connected_components(g))
# then, pull out each of these connected components
# and sort them by the number of nodes in them
graphs = list(nx.connected_component_subgraphs(g))
graphsSorted = sorted(graphs, key=len, reverse=True)
# for the top five largest graphs, print the number of nodes
# and draw the graph in a file
i = 0
for graph in graphsSorted[0:5]:
i += 1
print("num nodes in graph", i, ":", nx.number_of_nodes(graph))
graphDegree = nx.degree(graph)
# draw one set with name labels
f1 = plt.figure()
nx.draw(graph,
node_size=[v * 10 for v in graphDegree.values()],
with_labels=True,
font_size=8)
filename1 = 'graphLabels' + str(i) + '.png'
f1.savefig(filename1)
# draw one set without name labels
f2 = plt.figure()
nx.draw(graph, node_size=[v * 10 for v in graphDegree.values()])
filename2 = 'graph' + str(i) + '.png'
def l_Connection_strength(G):
l_Connection_strength_Dic = {}
node_set = G.nodes()
Connection_num = 0
#_l阶连通图的数量
#print nid,i_2_nei
for nid in node_set:
degree = G.degree(nid)
Neighbor_Set = G.neighbors(nid)
#print nid,Neighbor_Set
#print len(Neighbor_Set)
# i__nei=set(G.neighbors(i))
###current_1_neighbor=G.neighbors(nid)
#print nid,current_1_neighbor
###current_2_neighbor=[]
###for nnid in current_1_neighbor:
###current_2_neighbor = list(set(current_2_neighbor).union(set(G.neighbors(nnid))))
#print '2_hop:', nid,current_2_neighbor
###current_2_neighbor= list( set(current_2_neighbor).difference( set(current_1_neighbor).union(set([nid])) ) )
#print nid ,current_2_neighbor
#print nid,Neighbor_Set
if len(Neighbor_Set) == 1:
Connection_num = 1
#print nid
l_Connection_strength_Dic[nid] = 1.0
#print nid,l_Connection_strength_Dic[nid]
elif len(Neighbor_Set) == 0:
l_Connection_strength_Dic[nid] = 0.0
elif len(Neighbor_Set) > 1:
G_conn = nx.Graph()
#print nid, Neighbor_Set
##vi,j组合
Cluster_head_connection_set = []
for i in range(0, len(Neighbor_Set)):
#vi目标节点的邻居
vi = Neighbor_Set[i]
#print nid,Neighbor_Set[i]
n_vi_2 = []
##n_vi 是vi的邻居
for n_vi in G.neighbors(vi):
n_vi_2 = list(set(n_vi_2).union(set(G.neighbors(n_vi))))
n_vi_2 = list(
set(n_vi_2).difference(
set(G.neighbors(vi)).union(set([nid]))))
for j in range(i + 1, len(Neighbor_Set)):
vj = Neighbor_Set[j]
#print vi,vj
fai_ij = list(
set(n_vi_2).intersection(set(G.neighbors(vj))))
#print vi,vj,fai_ij
if fai_ij:
Cluster_head_connection_set.append(list([vi, vj]))
#
#print nid,Cluster_head_connection_set
for k in Cluster_head_connection_set:
G_conn.add_edge(k[0], k[1])
H = len(list(nx.connected_components(G_conn)))
#print nid,H
G_conn_nodenums = int(nx.number_of_nodes(G_conn))
##独立簇的数量
independent_cluster_num = int(
len(Neighbor_Set)) - int(G_conn_nodenums)
##l-阶的连通数
Connection_num = int(H) + int(independent_cluster_num)
l_Connection_strength_Dic[nid] = round(
float(Connection_num) / float(len(Neighbor_Set)), 3)
#print nid,l_Connection_strength_Dic[nid]
return l_Connection_strength_Dic
def get_stats(g, name):
print("\n\nGetting Statistics for: "+name)
print("Number of Nodes: "+str(networkx.number_of_nodes(g)))
print("Number of Edges: "+str(networkx.number_of_edges(g)))
print("Avg Clustering Coefficient: "+str(networkx.average_clustering(g)))
def commonwalkkernel(*args,
node_label='atom',
edge_label='bond_type',
# n=None,
weight=1,
compute_method=None,
n_jobs=None,
verbose=True):
"""Calculate common walk graph kernels between graphs.
Parameters
----------
Gn : List of NetworkX graph
List of graphs between which the kernels are calculated.
G1, G2 : NetworkX graphs
Two graphs between which the kernel is calculated.
node_label : string
Node attribute used as symbolic label. The default node label is 'atom'.
edge_label : string
Edge attribute used as symbolic label. The default edge label is 'bond_type'.
weight: integer
Weight coefficient of different lengths of walks, which represents beta
in 'exp' method and gamma in 'geo'.
compute_method : string
Method used to compute walk kernel. The Following choices are
available:
'exp': method based on exponential serials applied on the direct
product graph, as shown in reference [1]. The time complexity is O(n^6)
for graphs with n vertices.
'geo': method based on geometric serials applied on the direct product
graph, as shown in reference [1]. The time complexity is O(n^6) for
graphs with n vertices.
n_jobs : int
Number of jobs for parallelization.
Return
------
Kmatrix : Numpy matrix
Kernel matrix, each element of which is a common walk kernel between 2
graphs.
"""
# n : integer
# Longest length of walks. Only useful when applying the 'brute' method.
# 'brute': brute force, simply search for all walks and compare them.
compute_method = compute_method.lower()
# arrange all graphs in a list
Gn = args[0] if len(args) == 1 else [args[0], args[1]]
# remove graphs with only 1 node, as they do not have adjacency matrices
len_gn = len(Gn)
Gn = [(idx, G) for idx, G in enumerate(Gn) if nx.number_of_nodes(G) != 1]
idx = [G[0] for G in Gn]
Gn = [G[1] for G in Gn]
if len(Gn) != len_gn:
if verbose:
print('\n %d graphs are removed as they have only 1 node.\n' %
(len_gn - len(Gn)))
ds_attrs = get_dataset_attributes(
Gn,
attr_names=['node_labeled', 'edge_labeled', 'is_directed'],
node_label=node_label, edge_label=edge_label)
if not ds_attrs['node_labeled']:
for G in Gn:
nx.set_node_attributes(G, '0', 'atom')
if not ds_attrs['edge_labeled']:
for G in Gn:
nx.set_edge_attributes(G, '0', 'bond_type')
if not ds_attrs['is_directed']: # convert
Gn = [G.to_directed() for G in Gn]
start_time = time.time()
Kmatrix = np.zeros((len(Gn), len(Gn)))
# ---- use pool.imap_unordered to parallel and track progress. ----
def init_worker(gn_toshare):
global G_gn
G_gn = gn_toshare
# direct product graph method - exponential
if compute_method == 'exp':
do_partial = partial(wrapper_cw_exp, node_label, edge_label, weight)
# direct product graph method - geometric
elif compute_method == 'geo':
do_partial = partial(wrapper_cw_geo, node_label, edge_label, weight)
parallel_gm(do_partial, Kmatrix, Gn, init_worker=init_worker,
glbv=(Gn,), n_jobs=n_jobs, verbose=verbose)
# pool = Pool(n_jobs)
# itr = zip(combinations_with_replacement(Gn, 2),
# combinations_with_replacement(range(0, len(Gn)), 2))
# len_itr = int(len(Gn) * (len(Gn) + 1) / 2)
# if len_itr < 1000 * n_jobs:
# chunksize = int(len_itr / n_jobs) + 1
# else:
# chunksize = 1000
#
# # direct product graph method - exponential
# if compute_method == 'exp':
# do_partial = partial(wrapper_cw_exp, node_label, edge_label, weight)
# # direct product graph method - geometric
# elif compute_method == 'geo':
# do_partial = partial(wrapper_cw_geo, node_label, edge_label, weight)
#
# for i, j, kernel in tqdm(
# pool.imap_unordered(do_partial, itr, chunksize),
# desc='calculating kernels',
# file=sys.stdout):
# Kmatrix[i][j] = kernel
# Kmatrix[j][i] = kernel
# pool.close()
# pool.join()
# # ---- direct running, normally use single CPU core. ----
# # direct product graph method - exponential
# itr = combinations_with_replacement(range(0, len(Gn)), 2)
# if compute_method == 'exp':
# for i, j in tqdm(itr, desc='calculating kernels', file=sys.stdout):
# Kmatrix[i][j] = _commonwalkkernel_exp(Gn[i], Gn[j], node_label,
# edge_label, weight)
# Kmatrix[j][i] = Kmatrix[i][j]
#
# # direct product graph method - geometric
# elif compute_method == 'geo':
# for i, j in tqdm(itr, desc='calculating kernels', file=sys.stdout):
# Kmatrix[i][j] = _commonwalkkernel_geo(Gn[i], Gn[j], node_label,
# edge_label, weight)
# Kmatrix[j][i] = Kmatrix[i][j]
# # search all paths use brute force.
# elif compute_method == 'brute':
# n = int(n)
# # get all paths of all graphs before calculating kernels to save time, but this may cost a lot of memory for large dataset.
# all_walks = [
# find_all_walks_until_length(Gn[i], n, node_label, edge_label)
# for i in range(0, len(Gn))
# ]
#
# for i in range(0, len(Gn)):
# for j in range(i, len(Gn)):
# Kmatrix[i][j] = _commonwalkkernel_brute(
# all_walks[i],
# all_walks[j],
# node_label=node_label,
# edge_label=edge_label)
# Kmatrix[j][i] = Kmatrix[i][j]
run_time = time.time() - start_time
if verbose:
print("\n --- kernel matrix of common walk kernel of size %d built in %s seconds ---"
% (len(Gn), run_time))
return Kmatrix, run_time, idx
idxes = sorted(idxes, key=lambda x: len(nx.neighbors(G, x)), reverse=True)
pos = 0
while numCoveredEdges < nx.number_of_edges(G):
new_action = idxes[pos]
covered_set.add(new_action)
for neigh in nx.neighbors(G, new_action):
if neigh not in covered_set:
numCoveredEdges += 1
pos += 1
print 'done'
return len(covered_set)
'''
if __name__ == '__main__':
B = mmread('bcsstk01.mtx')
g_undirected = nx.from_scipy_sparse_matrix(B)
print(nx.number_of_nodes(g_undirected))
print(nx.number_of_edges(g_undirected))
api = VerelLib(sys.argv)
opt = {}
for i in range(1, len(sys.argv), 2):
opt[sys.argv[i][1:]] = sys.argv[i + 1]
# print greedy(g_undirected)
api.InsertGraph(g_undirected, is_test=True)
# startup
gen_new_graphs(opt)
for i in range(10):
api.lib.PlayGame(100, ctypes.c_double(1.0))
# node_list = discussion_graph.nodes()
with open("graph_edges.csv", "r") as edge_file:
for pair in edge_file:
edge = pair.split(';')
edge[1] = edge[1].strip()
try:
discussion_graph.node[edge[0]]['sender']
discussion_graph.node[edge[1]]['sender']
discussion_graph.add_edge(*edge)
except KeyError:
pass
edge_file.close()
print("Edges added.")
print("No. of Nodes: ", nx.number_of_nodes(discussion_graph))
print("No. of Edges: ", nx.number_of_edges(discussion_graph))
print("No. of Weakly Connected Components: ",
nx.number_weakly_connected_components(discussion_graph))
# Uncomment the lines below to save the graph as a GEXF file
# nx.write_gexf(discussion_graph, "gexf/master_disc_graph.gexf")
# print("GEXF file generated.")
# Uncomment the lines below to read the graph from a GEXF file
# discussion_graph = nx.read_gexf("gexf/master_disc_graph.gexf", node_type=int)
# print("Graph loaded from GEXF file.")
for conn_subgraph in nx.weakly_connected_component_subgraphs(discussion_graph):
sender_color_map = {}
node_list = [int(x) for x in conn_subgraph.nodes()]
def read_graph(self,
filename,
file_type='edgelist',
separator='\t',
remove_whitespace=False):
"""
Reads the graph from an edgelist, gml or graphml file and initializes the class attribute adjacency_matrix.
Parameters
----------
filename : string
Name of the file, for example 'JohnsHopkins.edgelist', 'JohnsHopkins.gml', 'JohnsHopkins.graphml'.
dtype : string
Type of file. Currently only 'edgelist', 'gml' and 'graphml' are supported.
Default = 'edgelist'
separator : string
used if file_type = 'edgelist'
Default = '\t'
remove_whitespace : bool
set it to be True when there is more than one kinds of separators in the file
Default = False
"""
if file_type == 'edgelist':
dtype = {0: 'int32', 1: 'int32', 2: 'float64'}
if remove_whitespace:
df = pd.read_csv(filename,
header=None,
dtype=dtype,
delim_whitespace=True)
else:
df = pd.read_csv(filename,
sep=separator,
header=None,
dtype=dtype)
source = df[0].values
target = df[1].values
if df.shape[1] == 2:
weights = np.ones(edges.shape[0])
elif df.shape[1] == 3:
weights = df[2].values
else:
raise Exception(
'graph_class_local.read_graph: df.shape[1] not in (2, 3)')
self._num_vertices = max(source.max() + 1, target.max() + 1)
#self.adjacency_matrix = source, target, weights
self.adjacency_matrix = sp.csr_matrix(
(weights, (source, target)),
shape=(self._num_vertices, self._num_vertices))
is_symmetric = (self.adjacency_matrix !=
self.adjacency_matrix.T).sum() == 0
if not is_symmetric:
# Symmetrize matrix, choosing larger weight
sel = self.adjacency_matrix.T > self.adjacency_matrix
self.adjacency_matrix = self.adjacency_matrix - self.adjacency_matrix.multiply(
sel) + self.adjacency_matrix.T.multiply(sel)
assert (self.adjacency_matrix !=
self.adjacency_matrix.T).sum() == 0
self._num_edges = self.adjacency_matrix.nnz
elif file_type == 'gml':
warnings.warn(
"Loading a gml is not efficient, we suggest using an edgelist format for this API."
)
G = nx.read_gml(filename).to_undirected()
self.adjacency_matrix = nx.adjacency_matrix(G).astype(np.float64)
self._num_edges = nx.number_of_edges(G)
self._num_vertices = nx.number_of_nodes(G)
elif file_type == 'graphml':
warnings.warn(
"Loading a graphml is not efficient, we suggest using an edgelist format for this API."
)
G = nx.read_graphml(filename).to_undirected()
self.adjacency_matrix = nx.adjacency_matrix(G).astype(np.float64)
self._num_edges = nx.number_of_edges(G)
self._num_vertices = nx.number_of_nodes(G)
else:
print('This file type is not supported')
return
self.compute_statistics()
G, top_nodes = bipartite_graph(df)
V = stock_network(G, top_nodes)
V = remove_edges(V, 0)
w = weight(V)
w = pd.DataFrame(w)
beta = w.quantile(q=0.1)
alpha = w.quantile(q=0.9)
print 'before remove any edges', nx.number_of_edges(V)
for u, v, data in V.edges(data=True):
#if data['weight'] < float(alpha):
if data['weight'] > float(beta):
V.remove_edge(u, v)
print 'after remove edges', nx.number_of_edges(V), nx.number_of_nodes(V)
remove = [node for node, degree in V.degree() if degree == 0]
V.remove_nodes_from(remove)
print 'after remove nodes with degree = 0', nx.number_of_nodes(V)
df = pd.DataFrame() #不是上面的df了
for nodes1, nodes2, data in V.edges(data=True):
w = 0
p = 0
w_df = []
if nodes1 != nodes2:
if len(list(nx.common_neighbors(G, nodes1, nodes2))) != 0:
for nbr in nx.common_neighbors(G, nodes1, nodes2):
p = p + 1
w1 = G[nodes1][nbr]['weight']
#print w1
def g_preprocess(G, alpha=1,
measures=["D1", "D2", "D3", "D4", "D5"]):
if isinstance(alpha, list) and len(alpha) == 5:
alphalist = alpha
elif isinstance(alpha, (int, float)):
alphalist = [alpha] * 5
else:
print(
"Error in the choice of alpha. "
"Please specify a single number or a list of 5 values."
)
return (
np.nan,
np.nan,
np.nan,
np.nan,
np.nan,
np.nan,
np.nan,
np.nan,
np.nan,
np.nan,
np.nan,
np.nan,
)
# Make an independent copy of the graph
G = G.copy()
if G.number_of_nodes() < 3:
print("Graph must have at least 3 nodes.")
return (
np.nan,
np.nan,
np.nan,
np.nan,
np.nan,
np.nan,
np.nan,
np.nan,
np.nan,
np.nan,
np.nan,
np.nan,
)
# From multigraph to graph
if type(G) == nx.MultiGraph:
print("MultiGraph converted to Graph")
G1 = nx.Graph()
G1.add_nodes_from(G.nodes(data=True))
for u, v, data in G.edges(data=True):
w = data["weight"] if "weight" in data else 1.0
if G1.has_edge(u, v):
G1[u][v]["weight"] += w
else:
G1.add_edge(u, v, weight=w)
G = G1.copy()
elif type(G) == nx.MultiDiGraph:
print("MultiDiGraph converted to DiGraph")
G1 = nx.DiGraph()
G1.add_nodes_from(G.nodes(data=True))
for u, v, data in G.edges(data=True):
w = data["weight"] if "weight" in data else 1.0
if G1.has_edge(u, v):
G1[u][v]["weight"] += w
else:
G1.add_edge(u, v, weight=w)
G = G1.copy()
# Remove Loops
loops = nx.selfloop_edges(G)
if list(loops):
print("WARNING: Loops will be ignored.")
G.remove_edges_from(loops)
# Check if all existing arcs have weights, otherwise assign value of 1
# Check for negative weights, zero weights and weight lower than 1
arcweights = [e[2]["weight"] for e in G.edges.data() if "weight" in e[2]]
numweights = len(arcweights)
arcweights = set(arcweights)
if any(w < 1 for w in arcweights):
print(
"Graph contains arcs with negative or zero weights,"
" or weights lower than 1. Weights must be >= 1."
)
if numweights != len(G.edges):
print(
"WARNING: weights are not specified for all arcs."
" Each arc must have a weight >= 1.\n"
"Missing weights are automatically set equal to 1."
)
for u, v, data in G.edges(data=True):
if "weight" not in data:
data["weight"] = 1
# Sums the weight of all arcs
totalWEI = 0
if "D3" in measures:
for u, v, data in G.edges(data=True):
totalWEI += data["weight"]
n1 = nx.number_of_nodes(G) - 1
# Calculates degree and weighted degree, taking alpha into account
if type(G) == nx.Graph:
if any(m in measures for m in ["D1", "D2", "D5"]):
deg = dict(nx.degree(G))
else:
deg = np.nan
indeg = outdeg = wei_insum_alpha_list = wei_outsum_alpha_list = np.nan
# Calculate weighted degree, taking alpha into account
# case of different alphas for each metric
if len(set(alphalist)) != 1:
wei_sum_alpha_list = [0, 0]
# Only needed for D3 and D4
if "D3" in measures:
wei_sum_alpha_list.append(weisumalpha (G, alphalist[2]))
else:
wei_sum_alpha_list += [0]
if "D4" in measures:
wei_sum_alpha_list.append(weisumalpha (G, alphalist[3]))
else:
wei_sum_alpha_list += [0]
wei_sum_alpha_list += [0]
else:
if any(m in measures for m in ["D3", "D4"]):
if alphalist[0] != 1:
wei_sum_alpha = {}
for node in G.nodes():
wei_sum_alpha[node] = sum(
[
e[2]["weight"] ** alphalist[0]
for e in list(G.edges(node, data=True))
]
)
else:
wei_sum_alpha = dict(nx.degree(G, weight="weight"))
else:
wei_sum_alpha = 0
wei_sum_alpha_list = (
[0, 0] + [wei_sum_alpha] * 2 + [0]
) # Only needed for D3 and D4
elif type(G) == nx.DiGraph:
deg = wei_sum_alpha_list = np.nan
if any(m in measures for m in ["D1", "D2", "D5"]):
indeg = dict(G.in_degree())
outdeg = dict(G.out_degree())
else:
indeg = outdeg = np.nan
if len(set(alphalist)) != 1:
wei_insum_alpha_list = [0, 0]
wei_outsum_alpha_list = [0, 0]
# Only needed for D3 and D4
if "D3" in measures:
insum, outsum = weiinoutsumalpha(G, alphalist[2])
wei_insum_alpha_list.append(insum)
wei_outsum_alpha_list.append(outsum)
else:
wei_insum_alpha_list.append(0)
wei_outsum_alpha_list.append(0)
if "D4" in measures:
insum, outsum = weiinoutsumalpha(G, alphalist[3])
wei_insum_alpha_list.append(insum)
wei_outsum_alpha_list.append(outsum)
else:
wei_insum_alpha_list.append(0)
wei_outsum_alpha_list.append(0)
wei_insum_alpha_list += [0]
wei_outsum_alpha_list += [0]
else:
if any(m in measures for m in ["D3", "D4"]):
if alphalist[0] != 1:
wei_outsum_alpha = {}
wei_insum_alpha = {}
for node in G.nodes():
wei_outsum_alpha[node] = sum(
[
e[2]["weight"] ** alphalist[0]
for e in list(G.out_edges(node, data=True))
]
)
wei_insum_alpha[node] = sum(
[
e[2]["weight"] ** alphalist[0]
for e in list(G.in_edges(node, data=True))
]
)
else:
wei_insum_alpha = dict(G.in_degree(weight="weight"))
wei_outsum_alpha = dict(G.out_degree(weight="weight"))
else:
wei_insum_alpha = wei_outsum_alpha = 0
wei_insum_alpha_list = [0, 0] + [wei_insum_alpha] * 2 + [0]
wei_outsum_alpha_list = [0, 0] + [wei_outsum_alpha] * 2 + [0]
# Calculate max and min arc weight
if G.number_of_edges() > 0:
hasedges = True
if any(m in measures for m in ["D1", "D3", "D4"]):
maxwij = max(dict(G.edges).items(),
key=lambda x: x[1]["weight"])[1]["weight"]
else:
maxwij = np.nan
if "D3" in measures:
minwij = min(dict(G.edges).items(),
key=lambda x: x[1]["weight"])[1]["weight"]
else:
minwij = np.nan
else:
print(
"Graph has no edges (remember that loops have been removed)."
"The function will return all zeros, regardless of normalizaiton."
)
hasedges = False
maxwij = np.nan
minwij = np.nan
return (
G,
n1,
deg,
indeg,
outdeg,
wei_insum_alpha_list,
wei_outsum_alpha_list,
wei_sum_alpha_list,
totalWEI,
maxwij,
minwij,
hasedges,
)
