def obtain_graph(args, suffix=""):
"""Build a Bipartite graph according to command line arguments
Arguments:
- `args`: command line options
"""
if getattr(args, "bp" + suffix) is not None:
l, r, p = getattr(args, "bp" + suffix)
G = bipartite_random_graph(l, r, p)
elif getattr(args, "bm" + suffix) is not None:
l, r, m = getattr(args, "bm" + suffix)
G = bipartite_gnmk_random_graph(l, r, m)
elif getattr(args, "bd" + suffix) is not None:
l, r, d = getattr(args, "bd" + suffix)
G = bipartite_random_left_regular(l, r, d)
elif getattr(args, "bregular" + suffix) is not None:
l, r, d = getattr(args, "bregular" + suffix)
G = bipartite_random_regular(l, r, d)
elif getattr(args, "bshift" + suffix) is not None:
N, M, pattern = getattr(args, "bshift" + suffix)
G = bipartite_shift(N, M, pattern)
elif getattr(args, "bcomplete" + suffix) is not None:
l, r = getattr(args, "bcomplete" + suffix)
G = complete_bipartite_graph(l, r)
# Workaround: the bipartite labels are missing in old version of networkx
for i in range(0, l):
G.add_node(i, bipartite=0)
for i in range(l, l + r):
G.add_node(i, bipartite=1)
elif getattr(args, "graphformat" + suffix) is not None:
try:
print("INFO: reading bipartite graph {} from '{}'".format(
suffix,
getattr(args, "input" + suffix).name),
file=sys.stderr)
G = readGraph(getattr(args, "input" + suffix), "bipartite",
getattr(args, "graphformat" + suffix))
except ValueError, e:
print("ERROR ON '{}'. {}".format(
getattr(args, "input" + suffix).name, e),
file=sys.stderr)
exit(-1)
def obtain_graph(args,suffix=""):
"""Build a Bipartite graph according to command line arguments
Arguments:
- `args`: command line options
"""
if getattr(args,"bp"+suffix) is not None:
l,r,p = getattr(args,"bp"+suffix)
G=bipartite_random_graph(l,r,p)
elif getattr(args,"bm"+suffix) is not None:
l,r,m = getattr(args,"bm"+suffix)
G=bipartite_gnmk_random_graph(l,r,m)
elif getattr(args,"bd"+suffix) is not None:
l,r,d = getattr(args,"bd"+suffix)
G=bipartite_random_left_regular(l,r,d)
elif getattr(args,"bregular"+suffix) is not None:
l,r,d = getattr(args,"bregular"+suffix)
G=bipartite_random_regular(l,r,d)
elif getattr(args,"bshift"+suffix) is not None:
N,M,pattern = getattr(args,"bshift"+suffix)
G=bipartite_shift(N,M,pattern)
elif getattr(args,"bcomplete"+suffix) is not None:
l,r = getattr(args,"bcomplete"+suffix)
G=complete_bipartite_graph(l,r)
# Workaround: the bipartite labels are missing in old version of networkx
for i in range(0,l):
G.add_node(i,bipartite=0)
for i in range(l,l+r):
G.add_node(i,bipartite=1)
elif getattr(args,"graphformat"+suffix) is not None:
try:
print("INFO: reading bipartite graph {} from '{}'".format(suffix,getattr(args,"input"+suffix).name),
file=sys.stderr)
G=readGraph(getattr(args,"input"+suffix),
"bipartite",
getattr(args,"graphformat"+suffix))
except ValueError,e:
print("ERROR ON '{}'. {}".format(getattr(args,"input"+suffix).name,e),file=sys.stderr)
exit(-1)
def obtain_graph(args):
"""Build a Bipartite graph according to command line arguments
Arguments:
- `args`: command line options
"""
if args.bp is not None:
l,r,p = args.bp
G=bipartite_random_graph(l,r,p)
elif args.bm is not None:
l,r,m = args.bm
G=bipartite_gnmk_random_graph(l,r,m)
elif args.bd is not None:
l,r,d = args.bd
G=bipartite_random_left_regular(l,r,d)
elif args.bregular is not None:
l,r,d = args.bregular
G=bipartite_random_regular(l,r,d)
elif args.bcomplete is not None:
l,r = args.bcomplete
G=complete_bipartite_graph(l,r)
elif args.graphformat is not None:
try:
G=readGraph(args.input, "bipartite", args.graphformat)
except ValueError,e:
print("ERROR ON '{}'. {}".format(args.input.name,e),file=sys.stderr)
exit(-1)
def synthetic_three_level(n1,n2,n3,p1,p2,p3,q1,q2,q3,no_isolates=True):
#,isolate_up=True,isolate_down=True):
#J_isolates=True,F_isolates=True,D_isolates=True
k=n1
J=nx.erdos_renyi_graph(n1,p1) #The first layer graph
Jis = nx.isolates(J)
F=nx.erdos_renyi_graph(n2,p2) #The second layer graph
Fis = nx.isolates(F)
D=nx.erdos_renyi_graph(n3,p3) #The third layer graph
Dis = nx.isolates(D)
H1=nx.bipartite_gnmk_random_graph(n1,n2,int(n1*n2*q1)) #The bipartite graph that bridges layers 1 and 2
# H1=nx.bipartite_random_graph(n1,n2,q1)
H2=nx.bipartite_gnmk_random_graph(n2,n3,int(n2*n3*q2)) #The bipartite graph that bridges layers 2 and 3
# H2=nx.bipartite_random_graph(n2,n3,q2)
H3=nx.bipartite_gnmk_random_graph(n3,n1,int(n1*n3*q3)) #The bipartite graph that bridges layers 1 and 3
# H3=nx.bipartite_random_graph(n3,n1,q3)
G=nx.Graph() #The synthetic two-layer graph
# Relabing nodes maps
mappingF={}
for i in range(n3+n2):
mappingF[i]=n1+i
HH2=nx.relabel_nodes(H2,mappingF,copy=True)
FF=nx.relabel_nodes(F,mappingF,copy=True)
mappingD={}
for i in range(n1+n3):
if i >n3-1:
mappingD[i]=i-n3
else:
mappingD[i]=n1+n2+i
HH3=nx.relabel_nodes(H3,mappingD,copy=True)
DD=nx.relabel_nodes(D,mappingD,copy=True)
# Removing isolates
if no_isolates:
J.remove_nodes_from(Jis)
H1.remove_nodes_from(Jis)
HH3.remove_nodes_from(Jis)
Fis = [mappingF[i] for i in Fis]
FF.remove_nodes_from(Fis)
H1.remove_nodes_from(Fis)
HH2.remove_nodes_from(Fis)
Dis = [mappingD[i] for i in Dis]
DD.remove_nodes_from(Dis)
HH2.remove_nodes_from(Dis)
HH3.remove_nodes_from(Dis)
# if J_isolates:
# J.remove_nodes_from(Jis)
# H1.remove_nodes_from(Jis)
# HH3.remove_nodes_from(Jis)
# if F_isolates:
# Fis = [mappingF[i] for i in Fis]
# FF.remove_nodes_from(Fis)
# H1.remove_nodes_from(Fis)
# HH2.remove_nodes_from(Fis)
# if D_isolates:
# Dis = [mappingD[i] for i in Dis]
# DD.remove_nodes_from(Dis)
# HH2.remove_nodes_from(Dis)
# HH3.remove_nodes_from(Dis)
G.add_edges_from(J.edges())
G.add_edges_from(H1.edges())
G.add_edges_from(DD.edges())
G.add_edges_from(HH2.edges())
G.add_edges_from(HH3.edges())
G.add_edges_from(FF.edges())
edgeList = []
for e in H1.edges():
edgeList.append(e)
for e in HH2.edges():
edgeList.append(e)
for e in HH3.edges():
edgeList.append(e)
return G, J, FF, DD, edgeList
def synthetic_three_level(n1,
n2,
n3,
p1,
p2,
p3,
q1,
q2,
q3,
no_isolates=True):
k = n1
J = nx.erdos_renyi_graph(n1, p1) #The first layer graph
Jis = nx.isolates(J)
F = nx.erdos_renyi_graph(n2, p2) #The second layer graph
Fis = nx.isolates(F)
D = nx.erdos_renyi_graph(n3, p3) #The third layer graph
Dis = nx.isolates(D)
H1 = nx.bipartite_gnmk_random_graph(n1, n2, int(
n1 * n2 * q1)) #The bipartite graph that bridges layers 1 and 2
H2 = nx.bipartite_gnmk_random_graph(n2, n3, int(
n2 * n3 * q2)) #The bipartite graph that bridges layers 2 and 3
H3 = nx.bipartite_gnmk_random_graph(n3, n1, int(
n1 * n3 * q3)) #The bipartite graph that bridges layers 1 and 3
G = nx.Graph() #The synthetic two-layer graph
# Relabing nodes maps
mappingF = {}
for i in range(n3 + n2):
mappingF[i] = n1 + i
HH2 = nx.relabel_nodes(H2, mappingF, copy=True)
FF = nx.relabel_nodes(F, mappingF, copy=True)
mappingD = {}
for i in range(n1 + n3):
if i > n3 - 1:
mappingD[i] = i - n3
else:
mappingD[i] = n1 + n2 + i
HH3 = nx.relabel_nodes(H3, mappingD, copy=True)
DD = nx.relabel_nodes(D, mappingD, copy=True)
# Removing isolates
if no_isolates:
J.remove_nodes_from(Jis)
H1.remove_nodes_from(Jis)
HH3.remove_nodes_from(Jis)
Fis = [mappingF[i] for i in Fis]
FF.remove_nodes_from(Fis)
H1.remove_nodes_from(Fis)
HH2.remove_nodes_from(Fis)
Dis = [mappingD[i] for i in Dis]
DD.remove_nodes_from(Dis)
HH2.remove_nodes_from(Dis)
HH3.remove_nodes_from(Dis)
G.add_edges_from(J.edges())
G.add_edges_from(H1.edges())
G.add_edges_from(DD.edges())
G.add_edges_from(HH2.edges())
G.add_edges_from(HH3.edges())
G.add_edges_from(FF.edges())
edgeList = []
for e in H1.edges():
edgeList.append(e)
for e in HH2.edges():
edgeList.append(e)
for e in HH3.edges():
edgeList.append(e)
return G, J, FF, DD, edgeList
def obtain_graph(args, suffix=""):
"""Build a Bipartite graph according to command line arguments
Arguments:
- `args`: command line options
"""
if getattr(args, "bp"+suffix) is not None:
l, r, p = getattr(args, "bp"+suffix)
G = bipartite_random_graph(l, r, p)
elif getattr(args, "bm"+suffix) is not None:
l, r, m = getattr(args, "bm"+suffix)
G = bipartite_gnmk_random_graph(l, r, m)
elif getattr(args, "bd"+suffix) is not None:
l, r, d = getattr(args, "bd"+suffix)
G = bipartite_random_left_regular(l, r, d)
elif getattr(args, "bregular"+suffix) is not None:
l, r, d = getattr(args, "bregular"+suffix)
G = bipartite_random_regular(l, r, d)
elif getattr(args, "bshift"+suffix) is not None:
N, M, pattern = getattr(args, "bshift"+suffix)
G = bipartite_shift(N, M, pattern)
elif getattr(args, "bcomplete"+suffix) is not None:
l, r = getattr(args, "bcomplete"+suffix)
G = complete_bipartite_graph(l, r)
# Workaround: the bipartite labels are missing in old version of networkx
for i in range(0, l):
G.add_node(i, bipartite=0)
for i in range(l, l+r):
G.add_node(i, bipartite=1)
elif getattr(args, "graphformat"+suffix) is not None:
try:
print("INFO: reading bipartite graph {} from '{}'".format(suffix, getattr(args, "input"+suffix).name),
file=sys.stderr)
G = readGraph(getattr(args, "input"+suffix),
"bipartite",
getattr(args, "graphformat"+suffix))
except ValueError as e:
print("ERROR ON '{}'. {}".format(
getattr(args, "input"+suffix).name, e), file=sys.stderr)
exit(-1)
else:
raise RuntimeError(
"Command line does not specify a bipartite graph")
# Graph modifications
if getattr(args, "plantbiclique"+suffix) is not None:
l, r = getattr(args, "plantbiclique"+suffix)
left, right = bipartite_sets(G)
if l > len(left) or r > len(right):
raise ValueError("Clique cannot be larger than graph")
left = random.sample(left, l)
right = random.sample(right, r)
for v, w in product(left, right):
G.add_edge(v, w)
if getattr(args, "addedges"+suffix) is not None:
k = getattr(args, "addedges"+suffix)
G.add_edges_from(sample_missing_edges(G, k))
if hasattr(G, 'name'):
G.name = "{} with {} new random edges".format(G.name, k)
# Output the graph is requested
if getattr(args, "savegraph"+suffix) is not None:
writeGraph(G,
getattr(args, "savegraph"+suffix),
'bipartite',
getattr(args, "graphformat"+suffix))
return G
def setup(self):
self.G = nx.bipartite_gnmk_random_graph(40, 60, 50)
self.M = nx.bipartite.biadjacency_matrix(self.G, range(40))
def setup(self):
self.G = nx.bipartite_gnmk_random_graph(40, 60, 50)
self.M = nx.bipartite.biadjacency_matrix(self.G, range(40))
def utest3():
G = NX.bipartite_gnmk_random_graph(20, 10, 50)
AM = NX.to_numpy_matrix(G).tolist()
write_jsongraph("gd1.json", adjmatrix_tojson(AM))
