def create_graph_kronecker(objects,
properties): #uses alpha-beta method to build kron
initEdges = properties['init_edges']
alpha = properties['alpha']
beta = properties['beta']
k = properties['kron_interations']
tryAgain = True
init = InitMatrix(properties['kron_seed_nodes'])
init.make()
init.addSelfEdges()
for i in range(len(initEdges)):
pos = initEdges[i]
index1 = pos[0]
index2 = pos[1]
if (index1 > properties['kron_seed_nodes']
or index2 > properties['kron_seed_nodes']):
raise IOError("init_edges are out of range of seed matrix size")
else:
init.addEdge(index1, index2)
if (properties['kron_seed_self_loops_stochastic'] == False):
init.makeStochasticAB(alpha, beta, False)
else:
init.makeStochasticAB(alpha, beta)
while (
tryAgain == True
): #loop to make sure we are connected, should there be a limit to this?
final = KroneckerGenerator.generateStochasticKron(init, k, True)
is_conn = nx.is_connected(final)
if (is_conn == False):
print "Graph was not connected, trying again..."
tryAgain = True
else:
tryAgain = False
stats = create_graph_stats(final)
conn = nx.Graph()
for (i, j) in final.edges():
inode = objects[i]
jnode = objects[j]
conn.add_edge(inode, jnode)
return conn, stats
def create_graph_kronecker(objects, properties): # uses alpha-beta method to build kron
initEdges = properties["init_edges"]
alpha = properties["alpha"]
beta = properties["beta"]
k = properties["kron_interations"]
tryAgain = True
init = InitMatrix(properties["kron_seed_nodes"])
init.make()
init.addSelfEdges()
for i in range(len(initEdges)):
pos = initEdges[i]
index1 = pos[0]
index2 = pos[1]
if index1 > properties["kron_seed_nodes"] or index2 > properties["kron_seed_nodes"]:
raise IOError("init_edges are out of range of seed matrix size")
else:
init.addEdge(index1, index2)
if properties["kron_seed_self_loops_stochastic"] == False:
init.makeStochasticAB(alpha, beta, False)
else:
init.makeStochasticAB(alpha, beta)
while tryAgain == True: # loop to make sure we are connected, should there be a limit to this?
final = KroneckerGenerator.generateStochasticKron(init, k, True)
is_conn = nx.is_connected(final)
if is_conn == False:
print "Graph was not connected, trying again..."
tryAgain = True
else:
tryAgain = False
stats = create_graph_stats(final)
conn = nx.Graph()
for (i, j) in final.edges():
inode = objects[i]
jnode = objects[j]
conn.add_edge(inode, jnode)
return conn, stats
#Networkx Graph Gen as Seed Testing, not Stochastic after #G = nx.watts_strogatz_graph(5, 3, 0.1) #G = nx.hypercube_graph(3) #nx.draw(G) #plt.show() # if you want to visualize your seed graph first #init = InitMatrix(nodes) #init = init.makeFromNetworkxGraph(G) #init.addSelfEdges() # if you want to ensure self edges for Kronecker k = 5 print "Seed Matrix Nodes:" print nodes print "Kronecker Iterations:" print k nxgraph = KroneckerGenerator.generateStochasticKron(init, k, True) #for line in nx.generate_edgelist(nxgraph, data=False): # print(line) print "Done Creating Network!" is_bipart = nx.is_bipartite(nxgraph) print "is_bipart:" print is_bipart is_conn = nx.is_connected(nxgraph) print "is_conn:" print is_conn #test #print "Exporting to GML File" #nx.write_gml(nxgraph,"KronSeed1_75a5b.gml") #export to gml file for gephi #nx.draw(nxgraph, pos=nx.spring_layout(nxgraph))
#Networkx Graph Gen as Seed Testing, not Stochastic after
#G = nx.watts_strogatz_graph(5, 3, 0.1)
#G = nx.hypercube_graph(3)
#nx.draw(G)
#plt.show() # if you want to visualize your seed graph first
#init = InitMatrix(nodes)
#init = init.makeFromNetworkxGraph(G)
#init.addSelfEdges() # if you want to ensure self edges for Kronecker
k = 5
print ("Seed Matrix Nodes:")
print (nodes)
print ("Kronecker Iterations:")
print (k)
nxgraph = KroneckerGenerator.generateStochasticKron(init, k, True)
#for line in nx.generate_edgelist(nxgraph, data=False):
# print(line)
print ("Done Creating Network!")
is_bipart = nx.is_bipartite(nxgraph)
print ("is_bipart:")
print (is_bipart)
is_conn = nx.is_connected(nxgraph)
print ("is_conn:")
print (is_conn) #test
#print "Exporting to GML File"
#nx.write_gml(nxgraph,"KronSeed1_75a5b.gml") #export to gml file for gephi
#nx.draw(nxgraph, pos=nx.spring_layout(nxgraph))
