def update_all_parameter(diff):
#print 'each difference - %s' % diff
luc_node = int(30*diff)
hcc_node = int(5)
time = 10
#parameter
luc_gro = int(6*diff)
hcc_gro = int(2)
lucG = nx.barabasi_albert_graph(luc_node, luc_gro)
hccG = nx.barabasi_albert_graph(hcc_node, hcc_gro)
frequency = np.array([0.9, 0.1])
G_combine =nx.Graph()
G_combine = graph.merge_graph(G_combine, hccG, lucG, frequency)
frequency_1 = np.array([0.5, 0.5])
G_combine_1 =nx.Graph()
G_combine_1 = graph.merge_graph(G_combine_1, hccG, lucG, frequency_1)
#Time series cell volume
LucN = []
hccN = []
#Number of initial cell
LucN0 = 100
hccN0 = 100
LucN_init = 100
hccN_init = 100
for t in range(time):
LucN.append(calc.convert_volume(LucN0))
lucG = graph.update_graph(lucG, luc_gro)
LucN0 = LucN_init*calc.calc_entropy(lucG, t+1)
for t in range(time):
hccN.append(calc.convert_volume(hccN0))
hccG = graph.update_graph(hccG, hcc_gro)
hccN0 = hccN_init*calc.calc_entropy(hccG, t+1)
#Mix Number of cell
MixN0 = 100
MixN_init = 100
initial_populations = MixN0*frequency
G_comb_gro = ((frequency*np.array([luc_gro, hcc_gro])).sum())/2
MixN = []
x = []
for t in range(time):
x.append(t)
MixN.append(calc.convert_volume(MixN0))
G_combine = graph.update_graph(G_combine, G_comb_gro)
MixN0 = MixN_init*calc.calc_entropy(G_combine, t+1)
sim_ratio = np.array(LucN)/np.array(MixN)
return sim_ratio
"""
def setUp(self):
self.G=nx.barabasi_albert_graph(1000, 10, 8)
pth_graph=nx.path_graph(1000)
edge=random.choice(pth_graph.edges())
pth_graph.remove_edge(edge[0], edge[1])
self.dG=pth_graph
self.dG2=nx.barabasi_albert_graph(900, 10, 0)
for i in xrange(900,1000):
self.dG2.add_node(i)
def test_powerlaw_mle():
print 'Testing Power law MLE estimator'
G = nx.barabasi_albert_graph(100, 5)
print 'nn: %d, alpha: %f'%(G.number_of_nodes(),powerlaw_mle(G))
G = nx.barabasi_albert_graph(1000, 5)
print 'nn: %d, alpha: %f'%(G.number_of_nodes(),powerlaw_mle(G))
G = nx.barabasi_albert_graph(10000, 5)
print 'nn: %d, alpha: %f'%(G.number_of_nodes(),powerlaw_mle(G))
G = nx.barabasi_albert_graph(100000, 5)
print 'nn: %d, alpha: %f'%(G.number_of_nodes(),powerlaw_mle(G))
print 'Expected: 2.9 (or thereabout)'
def main():
msg = "usage: ./p2p2012.py type r|g|g2 ttl par tries churn_rate"
if len(sys.argv) < 7:
print msg
sys.exit(1)
global out_file, churn_rate
out_file = sys.stdout
gtype = sys.argv[1]
walk = sys.argv[2]
ttl = int(sys.argv[3])
par = int(sys.argv[4])
tries = int(sys.argv[5])
churn_rate = float(sys.argv[6])
if gtype == "a":
g = nx.barabasi_albert_graph(97134, 3)
elif gtype == "b":
g = nx.barabasi_albert_graph(905668, 12)
elif gtype == "c":
g = sm.randomWalk_mod(97134, 0.90, 0.23)
elif gtype == "d":
g = sm.randomWalk_mod(905668, 0.93, 0.98)
elif gtype == "e":
g = sm.nearestNeighbor_mod(97134, 0.53, 1)
elif gtype == "f":
g = sm.nearestNeighbor_mod(905668, 0.90, 5)
elif gtype == "g":
g = nx.random_regular_graph(6, 97134)
elif gtype == "h":
g = nx.random_regular_graph(20, 905668)
elif gtype == "i":
g = nx.read_edgelist(sys.argv[7])
if walk == "r":
lookup(g, ttl, tries, par, get_random_node)
elif walk == "g":
lookup(g, ttl, tries, par, get_greedy_node)
elif walk == "g2":
lookup(g, ttl, tries, par, get_greedy2_node)
elif walk == "sum":
sum_edges(g, int(sys.argv[3]))
nodes = g.number_of_nodes()
edges = g.size()
avg_cc = nx.average_clustering(g)
print >> sys.stderr, nodes, edges, avg_cc
def test_networkx_matrix(self):
print('\n---------- Matrix Test Start -----------\n')
g = nx.barabasi_albert_graph(30, 2)
nodes = g.nodes()
edges = g.edges()
print(edges)
mx1 = nx.adjacency_matrix(g)
fp = tempfile.NamedTemporaryFile()
file_name = fp.name
sp.savetxt(file_name, mx1.toarray(), fmt='%d')
# Load it back to matrix
mx2 = sp.loadtxt(file_name)
fp.close()
g2 = nx.from_numpy_matrix(mx2)
cyjs_g = util.from_networkx(g2)
#print(json.dumps(cyjs_g, indent=4))
self.assertIsNotNone(cyjs_g)
self.assertIsNotNone(cyjs_g['data'])
self.assertEqual(len(nodes), len(cyjs_g['elements']['nodes']))
self.assertEqual(len(edges), len(cyjs_g['elements']['edges']))
# Make sure all edges are reproduced
print(set(edges))
diff = compare_edge_sets(set(edges), cyjs_g['elements']['edges'])
self.assertEqual(0, len(diff))
def compare_graphs(graph):
n = nx.number_of_nodes(graph)
m = nx.number_of_edges(graph)
k = np.mean(list(nx.degree(graph).values()))
erdos = nx.erdos_renyi_graph(n, p=m/float(n*(n-1)/2))
barabasi = nx.barabasi_albert_graph(n, m=int(k)-7)
small_world = nx.watts_strogatz_graph(n, int(k), p=0.04)
print(' ')
print('Compare the number of edges')
print(' ')
print('My network: ' + str(nx.number_of_edges(graph)))
print('Erdos: ' + str(nx.number_of_edges(erdos)))
print('Barabasi: ' + str(nx.number_of_edges(barabasi)))
print('SW: ' + str(nx.number_of_edges(small_world)))
print(' ')
print('Compare average clustering coefficients')
print(' ')
print('My network: ' + str(nx.average_clustering(graph)))
print('Erdos: ' + str(nx.average_clustering(erdos)))
print('Barabasi: ' + str(nx.average_clustering(barabasi)))
print('SW: ' + str(nx.average_clustering(small_world)))
print(' ')
print('Compare average path length')
print(' ')
print('My network: ' + str(nx.average_shortest_path_length(graph)))
print('Erdos: ' + str(nx.average_shortest_path_length(erdos)))
print('Barabasi: ' + str(nx.average_shortest_path_length(barabasi)))
print('SW: ' + str(nx.average_shortest_path_length(small_world)))
print(' ')
print('Compare graph diameter')
print(' ')
print('My network: ' + str(nx.diameter(graph)))
print('Erdos: ' + str(nx.diameter(erdos)))
print('Barabasi: ' + str(nx.diameter(barabasi)))
print('SW: ' + str(nx.diameter(small_world)))
def ba_network(p=25, n=150):
""" Barabasi-Albert algorithm is used to generate a scale free network. The
precision matrix is constructed as the above nearest neighbor based
algorithm. We also set the degree as 5."""
m = 5
G = networkx.barabasi_albert_graph(p, m) # obtain networkx library graph G
A = np.array(networkx.adjacency_matrix(G)) # numpy matrix is returned
# We weight the edges using a random number in [-1.0, -0.5] \cup [0.5, 1.0]
for i in range(p):
for j in range(p):
if A[i, j] > 0:
A[i, j] = A[i, j] * np.random.uniform(0.5, 1.0)
A[j, i] = A[i, j]
# ensure symmetry
A = (A + A.T) / 2.0
# Randomize sign
A = A * pow(-1.0, np.random.random_integers(0, 1, [p, p]))
# The diagonal entires are set to the sum of the absolute values of the row
# then, we obtain precision matrix
# I placed the factor 2 to ensure invertibility
P = A + 0.25 * np.diag(np.sum(np.absolute(A), 1))
# normalize entries to make the diagonal elements equal to one
P = P / np.diag(P)
cov = np.linalg.inv(P) # covariance matrix
# Sample from the covariance matrix
samples = np.random.multivariate_normal(np.zeros(p), cov, n)
return samples, cov
def test_random_model():
n_node = graph.number_of_nodes()
n_edge = graph.number_of_edges()
p = float(2 * n_edge) / (n_node*n_node - 2*n_node)
#new_graph = networkx.erdos_renyi_graph(n_node, p)
new_graph = networkx.barabasi_albert_graph(n_node, n_edge/n_node)
mapping = dict(zip(new_graph.nodes(), graph.nodes()))
new_graph = networkx.relabel_nodes(new_graph, mapping)
return new_graph
available_edges = graph.edges()
for edge in new_graph.edges():
if len(available_edges) > 0:
edge_org = available_edges.pop()
new_graph.add_edge(edge[0], edge[1], graph.get_edge(edge_org[0], edge_org[1]))
else:
print "Removing:", edge
new_graph.remove_edge(edge[0], edge[1])
for edge_org in available_edges:
print "Adding:", edge_org
new_graph.add_edge(edge_org[0], edge_org[1], graph.get_edge(edge_org[0], edge_org[1]))
return new_graph
def get_graph(objects, properties):
graph_type = properties['graph_type']
n = len(objects)-1
if 'num_nodes_to_attach' in properties.keys():
k = properties['num_nodes_to_attach']
else:
k = 3
r = properties['connection_probability']
tries = 0
while(True):
if graph_type == 'random':
x = nx.fast_gnp_random_graph(n,r)
elif graph_type == 'erdos_renyi_graph':
x = nx.erdos_renyi_graph(n,r)
elif graph_type == 'watts_strogatz_graph':
x = nx.watts_strogatz_graph(n, k, r)
elif graph_type == 'newman_watts_strogatz_graph':
x = nx.newman_watts_strogatz_graph(n, k, r)
elif graph_type == 'barabasi_albert_graph':
x = nx.barabasi_albert_graph(n, k, r)
elif graph_type == 'powerlaw_cluster_graph':
x = nx.powerlaw_cluster_graph(n, k, r)
elif graph_type == 'cycle_graph':
x = nx.cycle_graph(n)
else: ##Star by default
x = nx.star_graph(len(objects)-1)
tries += 1
cc_conn = nx.connected_components(x)
if len(cc_conn) == 1 or tries > 5:
##best effort to create a connected graph!
break
return x, cc_conn
def main():
### Undirected graph ###
# Initialize model using the Petersen graph
model=gmm.gmm(nx.petersen_graph())
old_graph=model.get_base()
model.set_termination(node_ceiling)
model.set_rule(rand_add)
# Run simualation with tau=4 and Poisson density for motifs
gmm.algorithms.simulate(model,4)
# View results
new_graph=model.get_base()
print(nx.info(new_graph))
# Draw graphs
old_pos=nx.spring_layout(old_graph)
new_pos=nx.spring_layout(new_graph,iterations=2000)
fig1=plt.figure(figsize=(15,7))
fig1.add_subplot(121)
#fig1.text(0.1,0.9,"Base Graph")
nx.draw(old_graph,pos=old_pos,node_size=25,with_labels=False)
fig1.add_subplot(122)
#fig1.text(0.1,0.45,"Simulation Results")
nx.draw(new_graph,pos=new_pos,node_size=20,with_labels=False)
fig1.savefig("undirected_model.png")
### Directed graph ###
# Initialize model using random directed Barabasi-Albert model
directed_base=nx.barabasi_albert_graph(25,2).to_directed()
directed_model=gmm.gmm(directed_base)
directed_model.set_termination(node_ceiling)
directed_model.set_rule(rand_add)
# Run simualation with tau=4 and Poisson density for motifs
gmm.algorithms.simulate(directed_model,4)
# View results
new_directed=directed_model.get_base()
print(nx.info(new_directed))
# Draw directed graphs
old_dir_pos=new_pos=nx.spring_layout(directed_base)
new_dir_pos=new_pos=nx.spring_layout(new_directed,iterations=2000)
fig2=plt.figure(figsize=(7,10))
fig2.add_subplot(211)
fig2.text(0.1,0.9,"Base Directed Graph")
nx.draw(directed_base,pos=old_dir_pos,node_size=25,with_labels=False)
fig2.add_subplot(212)
fig2.text(0.1,0.45, "Simualtion Results")
nx.draw(new_directed,pos=new_dir_pos,node_size=20,with_labels=False)
fig2.savefig("directed_model.png")
# Export files
nx.write_graphml(model.get_base(), "base_model.graphml")
nx.write_graphml(directed_model.get_base(), "directed_model.graphml")
nx.write_graphml(nx.petersen_graph(), "petersen_graph.graphml")
def generate(self): barabasi_albert = nx.barabasi_albert_graph(self.nodes, self.m, self.seed) self.nx_topology = nx.MultiDiGraph() self.nx_topology.clear() index = 0 nodes = [] for node in barabasi_albert.nodes(): #SSnodes.append(node+1) nodes.append(str(node+1)) self.nx_topology.add_nodes_from(nodes) for (n1, n2) in barabasi_albert.edges(): n1 = n1 + 1 n2 = n2 + 1 self.sip.update(str(index)) id_ = str(self.sip.hash()) #SSself.nx_topology.add_edge(n1, n2, capacity=self.DEFAULT_SPEED, allocated=0.0, src_port="", dst_port="", src_port_no="", dst_port_no="", src_mac="", dst_mac="", flows=[], id=id_) self.nx_topology.add_edge(str(n1), str(n2), capacity=self.DEFAULT_SPEED, allocated=0.0, src_port="", dst_port="", src_port_no="", dst_port_no="", src_mac="", dst_mac="", flows=[], id=id_) index = index + 1 self.sip.update(str(index)) id_ = str(self.sip.hash()) #SSself.nx_topology.add_edge(n2, n1, capacity=self.DEFAULT_SPEED, allocated=0.0, src_port="", dst_port="", src_port_no="", dst_port_no="", src_mac="", dst_mac="", flows=[], id=id_) self.nx_topology.add_edge(str(n2), str(n1), capacity=self.DEFAULT_SPEED, allocated=0.0, src_port="", dst_port="", src_port_no="", dst_port_no="", src_mac="", dst_mac="", flows=[], id=id_) index = index + 1
def generate_graph(type = 'PL', n = 100, seed = 1.0, parameter = 2.1):
if type == 'ER':
G = nx.erdos_renyi_graph(n, p=parameter, seed=seed, directed=True)
G = nx.DiGraph(G)
G.remove_edges_from(G.selfloop_edges())
elif type == 'PL':
z = nx.utils.create_degree_sequence(n, nx.utils.powerlaw_sequence, exponent = parameter)
while not nx.is_valid_degree_sequence(z):
z = nx.utils.create_degree_sequence(n, nx.utils.powerlaw_sequence, exponent = parameter)
G = nx.configuration_model(z)
G = nx.DiGraph(G)
G.remove_edges_from(G.selfloop_edges())
elif type == 'BA':
G = nx.barabasi_albert_graph(n, 3, seed=None)
G = nx.DiGraph(G)
elif type == 'grid':
G = nx.grid_2d_graph(int(np.ceil(np.sqrt(n))), int(np.ceil(np.sqrt(n))))
G = nx.DiGraph(G)
elif type in ['facebook', 'enron', 'twitter', 'students', 'tumblr', 'facebookBig']:
#print 'start reading'
#_, G, _, _ = readRealGraph(os.path.join("..","..","Data", type+".txt"))
_, G, _, _ = readRealGraph(os.path.join("..","Data", type+".txt"))
print 'size of graph', G.number_of_nodes()
#Gcc = sorted(nx.connected_component_subgraphs(G.to_undirected()), key = len, reverse=True)
#print Gcc[0].number_of_nodes()
#print 'num of connected components', len(sorted(nx.connected_component_subgraphs(G.to_undirected()), key = len, reverse=True))
#exit()
if G.number_of_nodes() > n:
G = getSubgraph(G, n)
#G = getSubgraphSimulation(G, n, infP = 0.3)
#nx.draw(G)
#plt.show()
return G
def __init__(self, N = 10000, m_0 = 3):
"""
:Purpose:
This is the base class used to generate the social network
for the other agents, i.e. . The class inherits from the PopulationClass.
:Input:
N : int
Number of agents. Default: 10000
m_0: int
Number of nodes each node is connected to in preferential
attachment step
"""
if type(N) is not int:
raise ValueError(('Population size must be integer,\
n = %s, not %s')%(string(N), type(N)))
else: pass
if m_0 not in range(10):
raise ValueError('m_0 must be integer smaller than 10')
else: self.m_0 = m_0
PopulationClass.__init__(self, n = N) # Create population
SpecialAgents = set(self.IDU_agents).union(set(self.MSM_agents))
SpecialAgents = SpecialAgents.union(set(self.NIDU_agents))
NormalAgents = set(range(self.PopulationSize)).difference(SpecialAgents)
NormalAgents = list(NormalAgents)
self.NetworkSize = len(NormalAgents)
# scale free Albert Barabsai Graph
self.G = nx.barabasi_albert_graph(self.NetworkSize,m_0)
def correlation_betweenness_degree_on_BA():
n = 1000
m = 2
G = nx.barabasi_albert_graph(n, m)
print nx.info(G)
ND, ND_lambda = ECT.get_number_of_driver_nodes(G)
print "ND = ", ND
print "ND lambda:", ND_lambda
ND, driverNodes = ECT.get_driver_nodes(G)
print "ND =", ND
degrees = []
betweenness = []
tot_degree = nx.degree_centrality(G)
tot_betweenness = nx.betweenness_centrality(G,weight=None)
for node in driverNodes:
degrees.append(tot_degree[node])
betweenness.append(tot_betweenness[node])
with open("results/driver_degree_BA.txt", "w") as f:
for x in degrees:
print >> f, x
with open("results/driver_betweenness_BA.txt", "w") as f:
for x in betweenness:
print >> f, x
with open("results/tot_degree_BA.txt", "w") as f:
for key, value in tot_degree.iteritems():
print >> f, value
with open("results/tot_betweenness_BA.txt", "w") as f:
for key, value in tot_betweenness.iteritems():
print >> f, value
def createScaleFreeNetwork(numOfNodes, degree):
'''
numOfNodes: The number of nodes that the scale free network should have
degree: The degree of the Scale Free Network
This function creates a Scale Free Network containing 'numOfNodes' nodes, each of degree 'degree'
It generates the required graph and saves it in a file. It runs the Reinforcement Algorithm to create a weightMatrix and an ordering of the vertices based on their importance by Flagging.
'''
global reinforce_time
G = nx.barabasi_albert_graph(numOfNodes, degree) #Create a Scale Free Network of the given number of nodes and degree
StrMap = {}
for node in G.nodes():
StrMap[node] = str(node)
G = nx.convert.relabel_nodes(G,StrMap)
print "Undergoing Machine Learning..."
start = time.time()
H = reinforce(G) #Enforce Machine Learning to generate a gml file of the learnt graph.
finish = time.time()
reinforce_time = finish - start
print "Machine Learning Completed..."
filename = "SFN_" + str(numOfNodes) + "_" + str(degree) + '.gpickle'
nx.write_gpickle(H,filename)#generate a gpickle file of the learnt graph.
print "Learnt graph Successfully written into " + filename
def generateRandomNetworks(randomSeed=622527):
seed(randomSeed)
# Network size will be 10^1, 10 ^2, 10^3, 10^4
for exponent in range(1, 4): # 1 .. 4
n = 10 ** exponent
for p in [0.1, 0.3, 0.5, 0.7, 0.9]:
m = round(n * p)
# Generate erdos Renyi networks
graph = nx.erdos_renyi_graph(n, p, randomNum())
graphName = "erdos_renyi_n{}_p{}.graph6".format(n, p)
nx.write_graph6(graph, directory + graphName)
# Generate Barabasi Albert networks
graph = nx.barabasi_albert_graph(n, m, randomNum())
graphName = "barabasi_albert_n{}_m{}.graph6".format(n, m)
nx.write_graph6(graph, directory + graphName)
for k in [0.1, 0.3, 0.5, 0.7, 0.9]:
k = round(n * k)
# Generate Watts Strogatz networks
graph = nx.watts_strogatz_graph(n, k, p, randomNum())
graphName = "watts_strogatz_n{}_k{}_p{}.graph6".format(n, k, p)
nx.write_graph6(graph, directory + graphName)
def barabasi_albert(N, M, seed, verbose=True):
'''Create random graph using Barabási-Albert preferential attachment model.
A graph of N nodes is grown by attaching new nodes each with M edges that
are preferentially attached to existing nodes with high degree.
Args:
N (int):Number of nodes
M (int):Number of edges to attach from a new node to existing nodes
seed (int) Seed for random number generator
Returns:
The NxN adjacency matrix of the network as a numpy array.
'''
A_nx = nx.barabasi_albert_graph(N, M, seed=seed)
A = nx.adjacency_matrix(A_nx).toarray()
if verbose:
print('Barbasi-Albert Network Created: N = {N}, '
'Mean Degree = {deg}'.format(N=N, deg=mean_degree(A)))
return A
def __init__(self, size, attachmentCount):
self.size = size
self.m = attachmentCount
self.network = nx.barabasi_albert_graph(self.size, self.m)
# Network stats object
self.networkStats = Networkstats(self.network, self.size)
def test_valid_degree_sequence2():
n = 100
for i in range(10):
G = nx.barabasi_albert_graph(n,1)
deg = list(G.degree().values())
assert_true( nx.is_valid_degree_sequence(deg, method='eg') )
assert_true( nx.is_valid_degree_sequence(deg, method='hh') )
def test083_barabasi_albert_graph(self):
""" Random graph using Barabasi-Albert preferential
attachment model. """
g = nx.barabasi_albert_graph(100, 5)
mate1 = mv.max_cardinality_matching(g)
mate2 = nx.max_weight_matching(g, True)
self.assertEqual(len(mate1), len(mate2))
def test_valid_degree_sequence2():
n = 100
for i in range(10):
G = nx.barabasi_albert_graph(n, 1)
deg = (d for n, d in G.degree())
assert_true(nx.is_valid_degree_sequence(deg, method="eg"))
assert_true(nx.is_valid_degree_sequence(deg, method="hh"))
def __init__(self, name, para1, para2):
if name == "PAM":
# nodes, edges, and nodes > edges
self.G = nx.barabasi_albert_graph(para1, para2)
# nodes, prob
elif name == "ER":
self.G = nx.erdos_renyi_graph(para1, para2)
def firstFitTopo(self, jobs, reservations):
if len(jobs) != len(reservations):
raise IndexError("Length of jobs and reservations input lists do no match")
import networkx as nx
skeleton = nx.barabasi_albert_graph(780)
G = nx.empty_graph()
def spread_size_distribution_vs_time():
probabilities = [float(i) / 10. for i in range(2, 7)]
graph = sm.SISModel.get_opera_graph()
seed = sm.SISModel.get_random_seed(graph, 100)
for t in [1, 5, 10]:
time_series = []
for i in xrange(len(probabilities)):
model = sm.SISModel(graph, seed, probabilities[i], t, True, 100)
model.spread()
time_series.append(model.get_time_series())
print(time_series)
sm.SISModel.plot_spread_size_distribution(
xrange(len(time_series[0])),
time_series,
['blue', 'red', 'green', 'orange', 'black'],
sm.SISModel.get_data_dir()
+ sm.SISModel.RESULT_DIR
+ 'o_spread_size_distribution_time_series_t{}.png'.format(str(t)),
't, steps'
)
graph = nx.barabasi_albert_graph(4604, 11)
seed = sm.SISModel.get_random_seed(graph, 100)
for t in [1, 5, 10]:
time_series = []
for i in xrange(len(probabilities)):
model = sm.SISModel(graph, seed, probabilities[i], t, True, 100)
model.spread()
time_series.append(model.get_time_series())
print(time_series)
sm.SISModel.plot_spread_size_distribution(
xrange(len(time_series[0])),
time_series,
['blue', 'red', 'green', 'orange', 'black'],
sm.SISModel.get_data_dir()
+ sm.SISModel.RESULT_DIR
+ 'ba_spread_size_distribution_time_series_t{}.png'.format(str(t)),
't, steps'
)
graph = nx.erdos_renyi_graph(4604, 0.0047)
seed = sm.SISModel.get_random_seed(graph, 100)
for t in [1, 5, 10]:
time_series = []
for i in xrange(len(probabilities)):
model = sm.SISModel(graph, seed, probabilities[i], t, True, 100)
model.spread()
time_series.append(model.get_time_series())
print(time_series)
sm.SISModel.plot_spread_size_distribution(
xrange(len(time_series[0])),
time_series,
['blue', 'red', 'green', 'orange', 'black'],
sm.SISModel.get_data_dir()
+ sm.SISModel.RESULT_DIR
+ 'er_spread_size_distribution_time_series_t{}.png'.format(str(t)),
't, steps'
)
def generate_graph(n, expected_degree, model="ba"):
"""
Generates a graph with a given model and expected_mean
degree
:param n: int Number of nodes of the graph
:param expected_degree: int Expected mean degree
:param model: string Model (ba, er, or ws)
:return: networkx graph
"""
global m
global ws_p
g = None
if model == "ba":
# BA expected avg. degree? m = ba_mean_degrees()
if m is None:
m = ba_mean_degrees(n, expected_degree)
g = nx.barabasi_albert_graph(n, m, seed=None)
if model == "er":
# ER expected avg. degree: d = p*(n-1)
p = float(expected_degree) / float(n - 1)
g = nx.erdos_renyi_graph(n, p, seed=None, directed=False)
if model == "ws":
# WS expected degree == k
g = nx.watts_strogatz_graph(n, expected_degree, ws_p)
return g
def createGraphsAndCommunities():
g = nx.scale_free_graph(500, alpha=0.40, beta=0.40, gamma=0.20)
g1 = nx.powerlaw_cluster_graph(500, 10, 0.2)
g2 = nx.barabasi_albert_graph(500, 10)
g3 = nx.newman_watts_strogatz_graph(500, 10, 0.2)
nx.write_graphml (g, direc+"sfg.graphml")
nx.write_graphml(g1, direc+"pcg.graphml")
nx.write_graphml(g2, direc+"bag.graphml")
nx.write_graphml(g3, direc+"nwsg.graphml")
graphs = {}
graphs["sfg"] = graph_tool.load_graph(direc+"sfg.graphml")
graphs["pcg"] = graph_tool.load_graph(direc+"pcg.graphml")
graphs["bag"] = graph_tool.load_graph(direc+"bag.graphml")
graphs["nwsg"] = graph_tool.load_graph(direc+"nwsg.graphml")
graphs["price"] = graph_tool.generation.price_network(1000)
for i,h in graphs.iteritems():
s = graph_tool.community.minimize_blockmodel_dl(h)
b = s.b
graph_tool.draw.graph_draw(h, vertex_fill_color=b, vertex_shape=b, output=direc+"block"+str(i)+".pdf")
com = graph_tool.community.community_structure(h, 10000, 20)
graph_tool.draw.graph_draw(h, vertex_fill_color=com, vertex_shape=com, output=direc+"community"+str(i)+".pdf")
state = graph_tool.community.minimize_nested_blockmodel_dl(h)
graph_tool.draw.draw_hierarchy(state, output=direc+"nestedblock"+str(i)+".pdf")
pagerank = graph_tool.centrality.pagerank(h)
graph_tool.draw.graph_draw(h, vertex_fill_color=pagerank, vertex_size = graph_tool.draw.prop_to_size(pagerank, mi=5, ma=15), vorder=pagerank, output=direc+"pagerank"+str(i)+".pdf")
h.set_reversed(is_reversed=True)
pagerank = graph_tool.centrality.pagerank(h)
graph_tool.draw.graph_draw(h, vertex_fill_color=pagerank, vertex_size = graph_tool.draw.prop_to_size(pagerank, mi=5, ma=15), vorder=pagerank, output=direc+"reversed_pagerank"+str(i)+".pdf")
def spread_size_distribution_vs_probability():
probabilities = [float(i) / 100. for i in range(100)]
t = 1
graph = sm.SISModel.get_opera_graph()
seed = sm.SISModel.get_random_seed(graph, 100)
print(seed)
o_sizes = []
for i in xrange(len(probabilities)):
print(probabilities[i])
o_sizes.append(sm.SISModel(graph, seed, probabilities[i], t).spread())
print(o_sizes)
graph = nx.barabasi_albert_graph(4604, 11)
seed = sm.SISModel.get_random_seed(graph, 100)
ba_sizes = []
for i in xrange(len(probabilities)):
ba_sizes.append(sm.SISModel(graph, seed, probabilities[i], t).spread())
print(ba_sizes)
graph = nx.erdos_renyi_graph(4604, 0.0047)
seed = sm.SISModel.get_random_seed(graph, 100)
er_sizes = []
for i in xrange(len(probabilities)):
er_sizes.append(sm.SISModel(graph, seed, probabilities[i], t).spread())
print(er_sizes)
sm.SISModel.plot_spread_size_distribution(
probabilities,
[o_sizes, ba_sizes, er_sizes],
['blue', 'black', 'red'],
sm.SISModel.get_data_dir() + sm.SISModel.RESULT_DIR + 'spread_size_distribution.png',
'p'
)
def generate_random_topology_BA(N_nodes, n):
"""Generate a Barabasi-Albert random graph with N_nodes nodes, and new link parameter n
Return the triplet (Graph, adjacancy matrix, Laplacian matrix)
"""
G = nx.barabasi_albert_graph(N_nodes, n)
adja = np.array(nx.to_numpy_matrix(G))
L = control2.get_Laplacian(adja)
return (G, adja, L)
def generate_network(self):
net_type_dict = dict(
complete=nx.complete_graph(self.N),
barabasi_albert=nx.barabasi_albert_graph(self.N,self.k),
watts_strogatz=nx.watts_strogatz_graph(self.N,self.k,
self.rewiring_prob),
)
return net_type_dict[self.net_type]
def preferentialAttachment(G):
n = G.number_of_nodes()
m = random.randrange(15,20)
PG = nx.barabasi_albert_graph(n,m)
plot(PG)
l = math.log(n)/math.log(math.log(n))
print 'Global Clustering: {0}\t'.format(str(nx.transitivity(PG))),
print 'Average path length : {0}\n'.format(str(l))
def diffusion():
"""Analyze similarities and differences
between simplified infection model and linear diffusion on
Barabasi-Albert networks.
Modify input and output as needed.
---Discussion---
The key points we will discuss here are:
- The mean of S, I, and V across all nodes, for various tau and D
- How long it takes for nodes to become 'infected'
- The effect of inital conditions on behaviour
We will compare how these behave on Barabasi-Albert graphs, for both a
simplified version of the infection model from part 2.2 (call this model 1),
and a linear diffusion model (call this model 2).
KEY POINT 1: mean of S, I, and V across all nodes, for various tau and D
(and simple starting conditions).
The parameter tau controls the strength of the flux matrix in model 1, whilst
the parameter D controls the strength of the linear diffusion in model 2.
Therefore it seems sensible to compare how these two parameters affect the
models' behaviour.
(We have chosen not to investigate the effect of varying theta0 in model 1,
as there is no clear equivalent in model 2 with which to compare it.)
Small values of tau or D: very little flux or diffusion
Figure 0 shows the mean value of S, I, and V in model 1 for some small
values of tau. The initial conditions are one infected node at (0.1, 0.05, 0.05)
as in part 2.1, the rest zero.
#plotmeans(RHS1, y01, [0.05, 0.1, 1], 100, 0)
(We ignore the source node itself when calculating the means, as this
tends to skew the outcome, and in any case we are more interested in the
spread of infected cells to the rest of the network than how the source
node behaves.)
We see that the numbers of spreaders and infected cells grow together,
whilst the number of vulnerable cells stays at or close to zero.
Looking at the simplified infection model's equations, this makes sense;
the summation terms will have no effect of interest, as they are independent
of one another and so just cause identical growth of each cell type
based on degree.
The nonlinear theta0*Si*Vi term is what triggers the behaviour - it is
negative in dVi/dt, meaning <V> will tend to shrink, whilst it is positive
in dIi/dt which will cause <I> to grow.
In particular, the bigger S is, the more <I> will grow, meaning its growth
can keep up with that of <S>. The exact ratio of growth speeds is decided
by theta=theta0, which does not depend on time (and is fixed at 80 here).
Figure 1 shows the same statistics, but for model 2.
#plotmeans(RHS2, y01, [0.01, 0.05, 0.1], 100, 1)
This time <S>, <I>, and <V> all follow the same behaviour, except <S> is
consistently twice as large as <I> and <V>. This makes sense considering
the linear diffusion model; growth of S, I, and V do not depend on their
current states, but only on the Laplacian matrix L (and the constant D).
Since <S> starts twice as high as the other two, this remains the case
under linear growth.
More precisely, we are repeatedly applying a constant matrix to the system,
so the system is tending to an equilibrium state at which the means do
not change. This corresponds to a distribution of S, I, and V which is
invariant under applying linear growth, i.e. corresponds to eigenvectors
of the system.
The lines in the plot flatten out over time as the values converge to
that of the equilibrium state.
(Note: below, model 2 is implemented with odeint rather than explicitly
finding eigenvectors, as it leads to nicer plot-making code for both models)
Medium and high values of tau or D: stronger flux or diffusion
Figures 2 and 3 show the same behaviour but with larger values of tau and D.
#plotmeans(RHS1, y01, [5, 30, 50], 100, 2)
#plotmeans(RHS2, y01, [1, 5, 10], 100, 3)
In model 1, a larger tau inevitably exaggerates the fast growth of <S>,
and hence of I. However, as tau becomes very large, we get a sudden burst
of vulnerable cells before they die out. This is most likely because at
first, the huge flux dominates any of the system's non-linear terms, meaning
high-degree nodes far from the infected source can spawn lots of cells
of all kinds. As soon as spreaders start to reach these nodes, however,
the vulnerable cells are smothered by the theta*S*V term and quickly
die out.
In model 2, a larger value of D simply speeds up the linear diffusion
process, which on the plots corresponds to quickly converging to the
equilibrium state. This helps confirm the analysis above.
#KEY POINT 2: how long it takes for nodes to become infected
(for simple starting conditions and a few tau or D)
Figures 4 and 5 (for models 1 and 2) show the fraction of all nodes which
are over K% infected, for various K. (This is over a larger timeframe than above.)
#plotfracs(RHS1, y01, 0.05, 4)
#plotfracs(RHS2, y01, 0.005, 5)
(tau and D control similar behaviour, but have different scaling factors;
they have been chosen here to display typical non-degenerate behaviours.)
In model 1, we see an initial burst in which many cells immediately become
more that 0.01% infected. After that, cells' infected percentages seem to
increase roughly linearly, with eventually all cells reaching 0.1% infection.
Model 2 shows almost identical behaviour, though it takes a bit longer
for the cells to become infected to each percentage.
The fact that both models show fairly steady increase of infected nodes is
not surprising; nodes tend to become infected when their neighbours are, which
will propagate through the graph.
A closer look shows that each line tends to sit still for a fraction of a second
before jumping up. This suggests that nodes tend to reach the next percentage
of infection in batches, i.e. that a connected cluster of nodes will all become
more infected at once (in both models).
In particular, since Barabasi-Albert models are scale-free, we would expect
some nodes with high degrees; once one of these becomes infeced, its many direct
neighbours will likely be infected soon as well, explaining the jumps on the plots.
#KEY POINT 3: effect of initial condition on behaviour
We will consider:
1) the case when five nodes are initially infected
2) the case when only the highest-degree node is initially infected.
Case 1: Figures 6 and 7 show the means of S, I, and V with five initially
infected nodes, in models 1 and 2 respectively.
#plotmeans(RHS1, y02, [0.1, 1, 5], 100, 6)
#plotmeans(RHS2, y02, [0.01, 0.1, 1], 100, 7)
These plots are most meaningful when compared to figures 0 and 1. However,
the values of the initially-infected nodes were not excluded this time when
calculating the means, so the graphs must be interpreted carefully.
In essence, we see the same behaviour (in terms of growth and ratios of the
three means), but occurring much faster. This is to be expected, as a greater
initial infection in five random nodes will inevitably be able to spread more
quickly than from one node.
In particular, in model 2 (figure 7), the snap to the equilibrium state is
very fast even for quite small parameter values.
The effect is more easily seen by observing the fraction of infected nodes;
figures 8 and 9 (in comparison to 4 and 5) show a much faster infection rate
than before.
#plotfracs(RHS1, y02, 0.05, 8)
#plotfracs(RHS2, y02, 0.005, 9)
Case 2: highest-degree node only is infected
As mentioned, the scale-free property of Barabasi-Albert models means they
are likely to have outlier nodes of very high degree. We would expect the
infection to happen faster than when a random node is initially infected.
Figures 10 to 13 are as above, with this initialy condition.
#plotmeans(RHS1, y03, [0.1, 1, 5], 100, 10)
#plotmeans(RHS2, y03, [0.01, 0.1, 1], 100, 11)
#plotfracs(RHS1, y03, 0.05, 12)
#plotfracs(RHS2, y03, 0.005, 13)
Plots 10 and 11 do show what we might expect; the same behaviour as in plots
0 to 3 (comparing the same tau) but faster and more exaggerated.
Interestingly, plots 12 and 13 show that the time it takes for nodes to become
infected is similar to that for a low-degree node initially infected, but
slower than for five initially infected nodes.
This might indicate that it is not the degree of infected nodes that affects
how quickly the illness spreads, but how many nodes are infected to start with.
(This effect could also happen if we were unlucky and randomly chose a high-
degree node earlier, but by taking several runs this was ensured not to be
the case.)
"""
#Barabasi-Albert graph and timestep array
N, M = 100, 5
BA = nx.barabasi_albert_graph(N, M)
tf, Nt = 40, 400
tarray = np.linspace(0, tf, Nt + 1)
#Setup for simplified infection model
theta0 = 80
A = nx.adjacency_matrix(BA).todense() #adjacency matrix
B = np.multiply(A, sum(A)).transpose() #ij entry is qi*Aij
F = np.nan_to_num(np.divide(
B, sum(B))) #not multiplied by tau yet as we will vary it
bigmat = sparse.block_diag((F, F, F)).toarray()
#Outputs RHS of simplified infection model, with given tau
def RHS1(y, t, tau):
S, V = y[:N], y[2 * N:] #don't need I to find the simplified model
dy = np.zeros(3 * N)
dy[N:2 * N] = theta0 * S * V
dy[2 * N:] = -theta0 * S * V
return dy - tau * y + tau * bigmat.dot(y)
#Outputs RHS of linear diffusion model
L = np.diag([BA.degree(i) for i in range(N)]) - A #Laplacian matrix
bigL = sparse.block_diag((L, L, L))
def RHS2(y, t, D):
return -D * bigL * y
#Solve specified system with given y0, and tau or D. Return things I want to plot
def solve(func, y0, tD, Ifrac):
#func is one of RHS1, RHS2
#tD is either tau (RHS1) or D (RHS2)
sol = odeint(func, y0, tarray, args=(tD, ))
#Find mean of each cell type at all times, not including the source node
Smean = [sol[i][1:N].mean() for i in range(Nt + 1)]
Imean = [sol[i][N + 1:2 * N].mean() for i in range(Nt + 1)]
Vmean = [sol[i][2 * N + 1:].mean() for i in range(Nt + 1)]
#Find fraction of nodes whose fraction of infected cells is over Ifrac
fracreached = [
sum(i > Ifrac for i in sublist) / float(N)
for sublist in sol[:, N:2 * N]
]
return (Smean, Imean, Vmean, fracreached)
#Plot means of S, I, V for specified model, y0, cutoff time, and tau or D. Save result
def plotmeans(func, y0, tDs, cutoff, fignum):
fig, axs = plt.subplots(1, 3, figsize=(15, 6))
sols = [[] for i in range(9)]
sols[0], sols[1], sols[2], _ = solve(func, y0, tDs[0], 0.1)
sols[3], sols[4], sols[5], _ = solve(func, y0, tDs[1], 0.1)
sols[6], sols[7], sols[8], _ = solve(func, y0, tDs[2], 0.1)
sols = [sublist[:cutoff] for sublist in sols]
x = tarray[:cutoff]
l1, l2, l3 = axs[0].plot(x, sols[0], x, sols[1], '.', x, sols[2])
l4, l5, l6 = axs[1].plot(x, sols[3], x, sols[4], '.', x, sols[5])
l7, l8, l9 = axs[2].plot(x, sols[6], x, sols[7], '.', x, sols[8])
axs[0].set_ylabel('Mean over all nodes in the network')
for i in range(3):
axs[i].set_xlabel('Time')
if func == RHS1:
axs[i].set_title("tau = " + str(tDs[i]))
if func == RHS2:
axs[i].set_title("D = " + str(tDs[i]))
fig.legend((l1, l2, l3), ('S', 'I', 'V'), 'center right')
if func == RHS1:
fig.suptitle(
'Simplified infection model: mean of S, I, and V over all nodes \n Christophe Jefferies \n Plot by diffusion: plotmeans'
)
if func == RHS2:
fig.suptitle(
'Linear diffusion model: mean of S, I, and V over all nodes \n Christophe Jefferies \n Plot by diffusion: plotmeans'
)
plt.savefig("fig" + str(fignum) + ".png", bbox_inches='tight')
#Plot fraction of infected nodes for specified model, y0, and tau or D. Save result
def plotfracs(func, y0, tD, fignum):
#Simplified infection model
fracs = [[] for i in range(10)] #will store output
for value in range(10):
(_, _, _, fracs[value]) = solve(func, y0, tD,
(value + 1) / float(10000))
#Make and save plot
plt.figure(figsize=(8, 8))
for i in range(10):
plt.plot(tarray, fracs[i], label=str((i + 1) / float(100)))
if func == RHS1:
plt.title(
'Simplified infection model: fraction of nodes over K% infected (tau = '
+ str(tD) +
')\n Christophe Jefferies \n Plot by diffusion: plotfracs')
if func == RHS2:
plt.title(
'Linear diffusion model: fraction of nodes over K% infected (D = '
+ str(tD) +
')\n Christophe Jefferies \n Plot by diffusion: plotfracs')
plt.xlabel('Time')
plt.ylabel('Fraction of nodes over K% infected')
plt.legend(title='K')
plt.savefig("fig" + str(fignum) + ".png", bbox_inches='tight')
#Define initial conditions
#Basic initial condition as in 2.1
piece1 = [1] + (N - 1) * [0.0]
y01 = piece1 + 2 * ([i / 2 for i in piece1])
#k nodes infected
k = 5
piece2 = [0.0 for i in range(N)]
for i in range(k):
piece2[int(i * N / float(k))] = 1
y02 = piece2 + 2 * ([i / 2 for i in piece2])
#Infect a node with highest degree
maxdeg = max([deg for (node, deg) in list(BA.degree())])
topnode = [node for node in range(N) if BA.degree(node) == maxdeg][0]
piece3 = [0.0 for i in range(N)]
piece3[topnode] = 1
y03 = piece3 + 2 * ([i / 2 for i in piece3])
plotmeans(RHS1, y01, [0.05, 0.1, 1], 100, 0)
plotmeans(RHS2, y01, [0.01, 0.05, 0.1], 100, 1)
plotmeans(RHS1, y01, [5, 30, 50], 100, 2)
plotmeans(RHS2, y01, [1, 5, 10], 100, 3)
plotfracs(RHS1, y01, 0.05, 4)
plotfracs(RHS2, y01, 0.005, 5)
plotmeans(RHS1, y02, [0.1, 1, 5], 100, 6)
plotmeans(RHS2, y02, [0.01, 0.1, 1], 100, 7)
plotfracs(RHS1, y02, 0.05, 8)
plotfracs(RHS2, y02, 0.005, 9)
plotmeans(RHS1, y03, [0.1, 1, 5], 100, 10)
plotmeans(RHS2, y03, [0.01, 0.1, 1], 100, 11)
plotfracs(RHS1, y03, 0.05, 12)
plotfracs(RHS2, y03, 0.005, 13)
def __init__(self,
structure='grid',
distribution='uniform',
dimensions=(2, 2),
custom_net=None,
rules=np.matrix([[0., 0.], [0., 0.]])):
# This will define a class that generates a grid of n by m elemets of agents
# Input Testing
if (isinstance(structure, str)):
self.rules = {}
self.define_rules(rules)
# Building a grid with dimensions specified by the args
if structure == 'grid':
if len(dimensions) < 3 and isinstance(
dimensions[0], int) and isinstance(dimensions[1], int):
self.network = nx.grid_2d_graph(dimensions[0],
dimensions[1],
periodic=True)
else:
ValueError('Too many arguments for option for grid')
elif structure == 'BA':
if len(dimensions) < 3 and isinstance(
dimensions[0], int) and isinstance(dimensions[1], int):
self.network = nx.barabasi_albert_graph(
dimensions[0], dimensions[1])
else:
ValueError('Too many arguments for option for grid')
elif structure == 'ER':
if len(dimensions) < 3 and isinstance(
dimensions[0], int) and isinstance(
dimensions[1], float):
self.network = nx.erdos_renyi_graph(
dimensions[0], dimensions[1])
else:
ValueError('Too many arguments for option for grid')
elif structure == 'custom':
if custom_net != None:
self.network = custom_net
else:
ValueError('Value for custom network was not provided')
else:
raise ValueError('Unknown option for structure')
if (isinstance(distribution, str)):
# Building a uniform distribution of strategies
if distribution == 'uniform':
cooperation_prob = {}
strategies = {}
for node in self.network.nodes():
cooperation_prob[node] = np.random.rand()
if cooperation_prob[node] > 0.5:
strategies[node] = 0
else:
strategies[node] = 1
# Setting the p attributes
nx.set_node_attributes(self.network,
strategies,
name='strategy')
# for node in self.network.nodes():
# print(nx.get_node_attributes(self.network,'strategy'))
else:
raise ValueError('Unknown option for distribution')
else:
raise TypeError('Option argument requires a string type')
from matplotlib import pyplot as plt
import networkx as nx
import numpy as np
import torch
from deep_graphs.utils.random_walks_metrics import score_matrix_from_random_walks, graph_from_scores
from deep_graphs.data.datasets import read_all_graphs
from deep_graphs.processes import node2vec_walk
number_of_nodes = 20
if __name__ == "__main__":
#BARABASI GRAPH
barabasi_graph = nx.barabasi_albert_graph(number_of_nodes, 3)
sparse_adjacency = nx.adjacency_matrix(barabasi_graph).tocoo()
#GENERATE GRAPHS
node2vec_walk(sparse_adjacency, 5)
walks = []
#READ FROM MODELS
for i in range(100):
walks.append(node2vec_walk(sparse_adjacency, 5))
walks = torch.Tensor(walks)
score_matrix = score_matrix_from_random_walks(walks,
number_of_nodes,
symmetric=True)
graph_adjacency_from_scores = graph_from_scores(
score_matrix, barabasi_graph.number_of_edges())
def generate_graphs(N,
n_subgraphs,
n_subgraph_nodes,
p_keep_edge=1,
density_multiplier=1,
n_duplicate_names=5,
force_connectivity=False):
assert n_subgraph_nodes <= N, "Subgraphs cannot be larger than the " + \
"underlying graph"
# Generate the underlying graph
G = nx.barabasi_albert_graph(N, 2)
n_uniq_names = int(np.ceil(N / float(n_duplicate_names)))
name_pool = []
for i in range(n_uniq_names):
name_pool += [i] * n_duplicate_names
for node in G.nodes():
G.node[node]['name'] = name_pool.pop(
random.randint(0,
len(name_pool) - 1))
#G.node[node]['entity'] = -1
base_density = nx.density(G)
#print "\nUnderlying graph density {}.\n".format(base_density)
# Compute the edge adding probability so that density is correctly
# multiplied
assert density_multiplier >= 1, "Density multiplier must be at least 1."
p_add_edge = base_density * (density_multiplier - 1)
sGs = [] # Subgraphs
for i in range(n_subgraphs):
sG = nx.Graph()
unexplored = [random.choice(G.nodes())]
while len(sG) < n_subgraph_nodes and len(unexplored) > 0:
next_node = random.choice(unexplored)
sG.add_node(next_node,
name=G.node[next_node]['name'],
entity=next_node,
subgraph=i,
id=next_node)
neighs = G.neighbors(next_node)
if not force_connectivity:
for neigh in neighs:
if not sG.has_node(neigh):
if neigh not in unexplored:
unexplored.append(neigh)
elif random.random() < p_keep_edge:
# Add neighbor
sG.add_edge(next_node, neigh)
else:
random.shuffle(neighs)
for ni, neigh in enumerate(neighs):
if not sG.has_node(neigh):
if neigh not in unexplored:
unexplored.append(neigh)
elif random.random() < p_keep_edge or sG.degree(
next_node) == 0:
# Add neighbor
sG.add_edge(next_node, neigh)
unexplored.remove(next_node)
density0 = nx.density(sG)
# Add edges to the subgraph
if p_add_edge > 0:
for u in sG.nodes():
for v in sG.nodes():
if v <= u:
# Don't try adding the same edge twice
continue
if not sG.has_edge(u, v) and random.random() < p_add_edge:
sG.add_edge(u, v)
density1 = nx.density(sG)
#print "Subgraph {} density after removal {} and after adding {}."\
# .format(i, density0, density1)
sGs.append(sG)
return sGs, G
Test = 0
n=1000 # número de nós
grau=2 # grau médio de ligação
iter=20# número de iterações
th=0.5 # threshold do cálculo de contaminação
#--------------------------------------
# Escolha da rede
#--------------------------------------
print('Qual topologia deseja utilizar?\n')
print(" 'B' - Barabasi \n 'N' - regular\n 'T' - From txt \n 'E' - Erdös-Rényi \n")
R = input('Topologia: ')
if R == "B" or R == "b": #BARABASI
G = nx.barabasi_albert_graph(n,grau)
elif R == "N": #REGULAR
G = nx.cycle_graph(n)
elif R == "T" or R == "t": #Por arquivo de Texto
G = nx.Graph()
#Adicionando os nós ao Grafo a partir do arquivo do professor.
os.chdir('C:\\Users\\Ellen')
#os.chdir('//storage//emulated//0')
#path = input('Digite o caminho completo (duas "//") até o arquivo ex: C://Eduardo//MODCOMP//grafos.dat')
#path = input('Digite o caminho completo (duas "//") até o arquivo ex: C://Users//Ellen//network.dat')
#os.chdir(path)
arestas_txt = open('network.dat','r')
def main():
import os
p = 0.7
delta = 1
parser = argparse.ArgumentParser()
parser.add_argument('-t',
'--type',
choices=all_graph_types,
help='graph type')
parser.add_argument('-s',
'--size',
type=int,
default=0,
help="size of graph")
parser.add_argument('-e',
'--size_exponent',
type=int,
default=1,
help="exponent of the size")
parser.add_argument('-b',
'--exponent_base',
type=int,
default=10,
help="base of the size exponent")
parser.add_argument('-n',
'--n_rounds',
type=int,
default=100,
help="number of simulated cascades")
args = parser.parse_args()
gtype = args.type
if args.size:
size = args.size
output_dir = 'data/{}/{}'.format(gtype, size)
else:
size = args.exponent_base**args.size_exponent
output_dir = 'data/{}/{}-{}'.format(gtype, args.exponent_base,
args.size_exponent)
if gtype == KRONECKER_HIER:
g = gen_kronecker(P=P_hier,
k=args.size_exponent,
n_edges=2**args.size_exponent * 3)
elif gtype == KRONECKER_PERI:
g = gen_kronecker(P=P_peri,
k=args.size_exponent,
n_edges=2**args.size_exponent * 3)
elif gtype == KRONECKER_RAND:
g = gen_kronecker(P=P_rand,
k=args.size_exponent,
n_edges=2**args.size_exponent * 3)
elif gtype == PL_TREE:
p = 0.88
g = random_powerlaw_tree(size, tries=999999)
elif gtype == B_TREE:
g = nx.balanced_tree(args.exponent_base, args.size_exponent - 1)
elif gtype == ER:
g = extract_larges_CC(nx.fast_gnp_random_graph(size, 0.1))
elif gtype == BARABASI:
g = extract_larges_CC(nx.barabasi_albert_graph(size, 5))
elif gtype == GRID:
g = grid_2d(int(np.sqrt(size)))
elif gtype == CLIQUE:
g = nx.complete_graph(size)
elif gtype == LINE:
g = nx.path_graph(size)
else:
raise ValueError('unsupported graph type {}'.format(gtype))
g.remove_edges_from(g.selfloop_edges())
print('|V|={}, |E|={}'.format(g.number_of_nodes(), g.number_of_edges()))
if gtype == GRID:
mapping = {(i, j): int(np.sqrt(size)) * i + j
for i, j in g.nodes_iter()}
g = nx.relabel_nodes(g, mapping)
else:
g = nx.convert_node_labels_to_integers(g)
if not os.path.exists(output_dir):
os.makedirs(output_dir)
print('graph type: {}'.format(gtype))
# g = add_p_and_delta(g, p, delta)
output_path = '{}/graph.graphml'.format(output_dir, gtype)
print('saving to {}'.format(output_path))
nx.write_graphml(g, output_path)
nx.write_gpickle(g, '{}/graph.gpkl'.format(output_dir, gtype))
if False:
pkl.dump(
time_probas,
open('{}/{}.pkl'.format(output_dir, INF_TIME_PROBA_FILE), 'wb'))
pkl.dump(node2id,
open('{}/{}.pkl'.format(output_dir, NODE2ID_FILE), 'wb'))
pkl.dump(id2node,
open('{}/{}.pkl'.format(output_dir, ID2NODE_FILE), 'wb'))
def test_short_example(tmpdir, request):
fname = "short_example.svg"
ref_fpath = os.path.join(TEST_DIR, fname)
test_fpath = str(tmpdir.join(fname))
# seed must be the same
random.seed(1)
# a network
g = networkx.barabasi_albert_graph(50, 2, seed=2)
# our hiveplot object
h = Hiveplot(test_fpath)
# start end
axis0 = Axis((200, 200), (200, 100), stroke="grey")
axis1 = Axis((200, 200), (300, 300), stroke="blue")
axis2 = Axis((200, 200), (10, 310), stroke="black")
h.axes = [axis0, axis1, axis2]
# randomly distribute nodes in axes
for n in g.nodes():
node = Node(n)
random.choice(h.axes).add_node(node, random.random())
for e in g.edges():
if (e[0] in axis0.nodes) and (
e[1] in axis1.nodes): # edges from axis0 to axis1
h.connect(axis0,
e[0],
45,
axis1,
e[1],
-45,
stroke_width='0.34',
stroke_opacity='0.4',
stroke='purple')
elif (e[0] in axis0.nodes) and (
e[1] in axis2.nodes): # edges from axis0 to axis2
h.connect(axis0,
e[0],
-45,
axis2,
e[1],
45,
stroke_width='0.34',
stroke_opacity='0.4',
stroke='red')
elif (e[0] in axis1.nodes) and (
e[1] in axis2.nodes): # edges from axis1 to axis2
h.connect(axis1,
e[0],
15,
axis2,
e[1],
-15,
stroke_width='0.34',
stroke_opacity='0.4',
stroke='magenta')
h.save(pretty=True)
try:
if sys.version_info >= (3, 6):
assert_same_contents(ref_fpath, test_fpath)
# elif (3, 0) < sys.version_info < (3, 6):
else:
# cannot check for exact identity in CPython < 3.6 due to
# non-deterministic dict ordering
assert_same_lines(ref_fpath, test_fpath)
except AssertionError:
if request.config.getoption("--dump"):
ver = '.'.join(str(i) for i in sys.version_info)
dt = datetime.now().isoformat().replace(':', '-')
shutil.copyfile(test_fpath,
os.path.join(TEST_DIR, '_'.join([ver, dt, fname])))
if sys.version_info < (3, 0):
pytest.xfail(
"Python 2 paths are visibly identical, but numerically different"
)
raise
def barabasi_albert():
g = nx.barabasi_albert_graph(10000, 5)
return g
def barabasi_albert(nodes, edges):
"""creates a network using the Barabasi-Albert model """
g.network = nx.barabasi_albert_graph(nodes, edges)
g.empty_nodes = g.network.nodes()
g.nodes_with_agents = []
g.neighbors_with_agents = {}
# write_json_graph(nx.balanced_tree(3, 5), 'balanced_tree(3,5).json')
write_json_graph(nx.balanced_tree(3, 6), 'balanced_tree(3,6).json')
# write_json_graph(nx.balanced_tree(3, 7), 'balanced_tree(3,7).json')
write_json_graph(nx.balanced_tree(3, 8), 'balanced_tree(3,8).json')
# write_json_graph(nx.balanced_tree(3, 10), 'balanced_tree(3,10).json')
write_json_graph(nx.balanced_tree(4, 4), 'balanced_tree(4,4).json')
# write_json_graph(nx.balanced_tree(4, 5), 'balanced_tree(4,5).json')
write_json_graph(nx.balanced_tree(4, 6), 'balanced_tree(4,6).json')
write_json_graph(nx.balanced_tree(4, 8), 'balanced_tree(4,7).json')
# write_json_graph(nx.balanced_tree(5, 2), 'balanced_tree(5,2).json')
# write_json_graph(nx.balanced_tree(5, 4), 'balanced_tree(5,4).json')
# write_json_graph(nx.barabasi_albert_graph(10, 5), 'barabasi_albert_graph(10,5).json')
# write_json_graph(nx.barabasi_albert_graph(20, 3), 'barabasi_albert_graph(20,3).json')
# write_json_graph(nx.barabasi_albert_graph(20, 10), 'barabasi_albert_graph(20,10).json')
write_json_graph(nx.barabasi_albert_graph(50, 40),
'barabasi_albert_graph(50,40).json')
# write_json_graph(nx.random_lobster(5, 0.6, 0.4, seed=0), 'random_lobster(5,0_6,0_4).json')
# write_json_graph(nx.random_lobster(5, 0.4, 0.6, seed=0), 'random_lobster(5,0_4,0_6).json')
write_json_graph(nx.random_lobster(10, 0.6, 0.4, seed=0),
'random_lobster(10,0_6,0_4).json')
# write_json_graph(nx.random_lobster(10, 0.4, 0.6, seed=0), 'random_lobster(10,0_4,0_6).json')
write_json_graph(nx.random_lobster(20, 0.6, 0.4, seed=0),
'random_lobster(20,0_6,0_4).json')
# write_json_graph(nx.random_lobster(20, 0.4, 0.6, seed=0), 'random_lobster(20,0_4,0_6).json')
write_json_graph(nx.random_lobster(50, 0.6, 0.4, seed=0),
'random_lobster(50,0_6,0_4).json')
# write_json_graph(nx.random_lobster(50, 0.4, 0.6, seed=0), 'random_lobster(50,0_4,0_6).json')
write_json_graph(nx.random_lobster(100, 0.6, 0.4, seed=0),
'random_lobster(100,0_6,0_4).json')
def BA(N,m,wseed): if wseed == 1: G = nx.barabasi_albert_graph(N, m, seed=123) else: G = nx.barabasi_albert_graph(N, m) return G
}, {
'delay': random.choice(e2e_latency),
'para': (32.595, 4.5222)
}, {
'delay': random.choice(e2e_latency),
'para': (86.982, 5.7892)
}, {
'delay': random.choice(e2e_latency),
'para': (39.205, 2.9545)
}]
numOfTan = 6 # this is a special parameter
# rdgraph = gp.fast_gnp_random_graph(NumberOfNode, conn_prob)
rdgraph = gp.barabasi_albert_graph(NumberOfNode, 2, seed=2)
# rdgraph = gp.barabasi_albert_graph(NumberOfNode, 20)
# rdgraph = gp.balanced_tree(2, 4)
# rdgraph = gp.gnp_random_graph(NumberOfNode, conn_prob)
node_list = range(0, NumberOfNode)
access_node_list = random.choice(node_list, NumberOfAccessNode, replace=False)
# pickle.dump(access_node_list, open("an_list.pickle", "wb"))
# # print "access list:", access_node_list
edge_node_list = [node for node in node_list if node not in access_node_list]
# pickle.dump(edge_node_list, open("en_list.pickle", "wb"))
import networkx as network
import networkx as nx
import matplotlib.pyplot as plot
import matplotlib.pyplot as plt
import math
import numpy as np
alpha=0.4
beta=0.3
nodenumber=50
initial_average_degree=1
graph1 = network.barabasi_albert_graph(nodenumber, initial_average_degree)
graph2 = network.barabasi_albert_graph(nodenumber, initial_average_degree)
ps1 = network.spring_layout(graph1)
ps2 = network.spring_layout(graph2)
start_edges=graph1.number_of_edges()+graph2.number_of_edges()#初始边数量
network.draw(graph1, ps1, with_labels = False, node_size = nodenumber)
#plt.savefig("A网络.png")
plot.show()
network.draw(graph2, ps2, with_labels = False, node_size = nodenumber)
#plt.savefig("B网络.png")
plot.show()
G = network.union(graph1, graph2, rename=('Gone', 'Gtwo'))
psG=network.spring_layout(G)
network.draw(G, psG, with_labels = False, node_size = 30)
#plt.savefig("组合网络.png")
plot.show()
###三种匹配方式
# Add some outliers to the system
num_outliers = 0
scio_auc = np.zeros(no_runs)
glasso_auc = np.zeros(no_runs)
threshold_auc = np.zeros(no_runs)
ns_auc = np.zeros(no_runs)
space_auc = np.zeros(no_runs)
clime_auc = np.zeros(no_runs)
#d_trace_auc = np.zeros(no_runs)
for i in range(no_runs):
if network_structure == "uniform":
K = make_sparse_spd_matrix(p, norm_diag=True)
elif network_structure == "power law":
L = nx.to_numpy_array(nx.barabasi_albert_graph(p, 5))
np.fill_diagonal(L, 1)
alpha = 0.8
K = (1 - alpha) * np.eye(p) + alpha * L
elif network_structure == "caveman":
no_cliques = int(p / 5)
L = nx.to_numpy_array(nx.caveman_graph(no_cliques, 5))
np.fill_diagonal(L, 1)
alpha = 0.8
K = (1 - alpha) * np.eye(p) + alpha * L
C = np.linalg.inv(K)
X = np.random.multivariate_normal(np.zeros(p), C, n)
if noise:
X += np.random.multivariate_normal(np.zeros(p), np.eye(p), n)
def graf():
q = 0;
while q == 0:
beta = float(raw_input("Podaj wartosc bety (predkosc rozprzestrzenania sie epidemii, beta > gamma): "))
gamma = float(raw_input("Podaj wartosc gamma (predkosc zdrowienia populacji, beta > gamma): "))
if beta > gamma:
q = 1
else:
print 'Nieprawidlowe dane! (beta > gamma; liczby zmiennoprzecinkowe)'
q = 0
N = int(raw_input("Podaj liczebnosc populacji: "))
czas = int(raw_input("Podaj czas trwania obserwacji epidemii: "))
options_2 = {
'with_labels': False,
'node_color': 'grey',
'node_size': 100,
'edge_color': 'grey',
'linewidths': 0,
'width': 0.3,
}
options_i = {
'with_labels': False,
'node_color': 'red',
'node_size': 100,
'edge_color': 'grey',
'linewidths': 0,
'width': 0.3,
}
options_r = {
'with_labels': False,
'node_color': 'blue',
'node_size': 100,
'edge_color': 'grey',
'linewidths': 0,
'width': 0.3,
}
nazwy=[]
i=[]
r=[]
graf = nx.barabasi_albert_graph(N,4) # graf w ksztalcie kuli, 4 oznacza kreski odchodzace od kulek ich zasieg
pos = nx.spring_layout(graf)
# pierwszy chory losowo
pierwszy = random.choice(graf.nodes())
graf.node[pierwszy] = 'I'
i.append(pierwszy)
for x in range(czas):
liczba = 0
while liczba < N:
liczba = liczba + 1
los = random.choice(graf.nodes()) # wybiera losowo kulke
if graf.node[los] == 'I': # chory - losowanie czy wyzdrowieje, czy zarazi (0.0,1.0)
if len(i) == 1:
sasiad = random.choice(graf.neighbors(los)) # los na sasiada
if graf.node[sasiad] != 'R' and graf.node[sasiad] != 'I':
# gdy sasiad jest zdrowy porownanie do szybkosci rozprzestrzeniania i zarazenie lub nie
if random.random() < beta:
graf.node[sasiad] = 'I'
else:
a = random.random()
if a < gamma:
graf.node[los] = 'R'
if a > gamma:
sasiad = random.choice(graf.neighbors(los)) # los na sasiada
if graf.node[sasiad] != 'R' and graf.node[sasiad] != 'I':
# gdy sasiad jest zdrowy porownanie do szybkosci rozprzestrzeniania i zarazenie lub nie
if random.random() < beta:
graf.node[sasiad] = 'I'
for no in graf.nodes():
if graf.node[no] == 'I':
i.append(no)
if graf.node[no] == 'R':
r.append(no)
else:
continue
plot.axis('off')
nx.draw_networkx_edges(graf,pos,edge_color='k')
nx.draw_networkx_nodes(graf, pos, **options_2)
nx.draw_networkx_nodes(graf, pos, nodelist=i,label=len(i), **options_i)
nx.draw_networkx_nodes(graf, pos, nodelist=r, **options_r)
p = str(len(i))
plot.title(x)
plot.annotate(p, xy=(2, 1), xytext=(3, 1.5))
filenames = str(x) + ".png"
plot.savefig(filenames)
plot.savefig(filenames)
nazwy.append(filenames)
images=[]
for nazwa in nazwy:
images.append(imageio.imread(nazwa))
imageio.mimsave('test.gif', images, duration=2, fps=100)
def generateBASyntheticGraph(
n_nodes,
n_edges): #generates a synthetic graph using the barabasi-albert model
return nx.barabasi_albert_graph(n_nodes, n_edges)
import sys
import networkx as nx
import seir
if __name__ == "__main__":
NET_SIZE = int(sys.argv[1]) # network size
M = int(sys.argv[2]) # min degree
E2I_rate = float(sys.argv[3]) # rate from E to I states
trans_rate = float(sys.argv[4]) # transmission rate
recov_rate = float(sys.argv[5]) # recovery rate
output_log_file = sys.argv[6]
output_net_file = sys.argv[7]
G = nx.barabasi_albert_graph(NET_SIZE, M)
params = {
"E2I_rate": E2I_rate,
"trans_rate": trans_rate, # transmission rate (for edges)
"recov_rate": recov_rate, # recovery rate (for nodes)
"init_seed_frac": 0.001, # initial seed fraction
}
sim_data = seir.run_SEIR(G, **params)
seir.to_log(G, sim_data, output_log_file)
nx.write_edgelist(G, output_net_file)
def sim_data(genes1, genes2, background, patients1, patients2, dens):
n = genes1 + genes2 + background
m = patients1 + patients2
genes = np.arange(n)
groups_genes = list(np.ones(genes1)) + list(np.ones(genes2) * 2) + list(
np.ones(background) * 3)
groups_p = [1 if node < patients1 else 2 for node in range(m)]
to_sparce = 0.3 #to sparcify bipartite
to_mix = 0.99 # to mix edges berween groups
b = np.zeros((n, m))
ge = np.random.normal(0, 1, n * m).reshape(n, m)
for patient in range(m):
for gene in range(n):
p_gr = groups_p[patient]
g_gr = groups_genes[gene]
if p_gr == 1 and g_gr == 1: #all up
ge[gene, patient] = np.random.normal(1, 0.35, 1)
elif p_gr == 2 and g_gr == 2:
ge[gene, patient] = np.random.normal(1, 0.35, 1) #also up
elif p_gr == 1 and g_gr == 2:
ge[gene, patient] = np.random.normal(-1, 0.35, 1) #down
elif p_gr == 2 and g_gr == 1:
ge[gene, patient] = np.random.normal(-1, 0.35, 1) #down
for patient in range(m):
for gene in range(genes1 + genes2):
prob = np.random.uniform(0, 1)
if prob > 0.9:
ge[gene, patient] = np.random.normal(0, 1, 1)
for gene in range(genes1 + genes2, n):
prob = np.random.uniform(0, 1)
if prob < 0.05:
for patient in range(m):
if groups_p[patient] == 1: #all up
ge[gene, patient] = np.random.normal(0.3, 0.35, 1)
else:
ge[gene, patient] = np.random.normal(-0.3, 0.35, 1)
if prob > 0.05 and prob < 0.1:
for patient in range(m):
if groups_p[patient] == 1: #all up
ge[gene, patient] = np.random.normal(-0.3, 0.35, 1)
else:
ge[gene, patient] = np.random.normal(0.3, 0.35, 1)
g1 = nx.barabasi_albert_graph(genes1, 1)
g2 = nx.barabasi_albert_graph(genes2, 1)
g3 = nx.barabasi_albert_graph(background, 1)
G = nx.disjoint_union(g1, g2)
G = nx.disjoint_union(G, g3)
for _ in range(int(dens * n)):
node1 = np.random.randint(0, genes1)
node2 = np.random.randint(genes1, genes1 + genes2)
node3_1 = np.random.randint(genes1 + genes2, n)
node3_2 = np.random.randint(genes1 + genes2, n)
G.add_edges_from([(node1, node3_1), (node2, node3_2)])
d = nx.density(G)
count = 0
while d > 0.002 and count < 10:
node3_1 = np.random.randint(genes1 + genes2, n)
node3_2 = np.random.randint(genes1 + genes2, n)
count = count + 1
if G.has_edge(node3_1, node3_2):
G.remove_edge(node3_1, node3_2)
d = nx.density(G)
#A_g = nx.adj_matrix(G).todense() *1
b_sp = csr_matrix(b) #sparse matrix for making bipartite graph
B = bipartite.from_biadjacency_matrix(b_sp)
GE = pd.DataFrame(ge, index=np.arange(n), columns=np.arange(n, n + m))
H = HI_big(GE, 1, 1, 1)
return (B, GE, G, H, d, n, m)
def create(args):
### load datasets
graphs=[]
# synthetic graphs
if args.graph_type=='ladder':
graphs = []
for i in range(100, 201):
graphs.append(nx.ladder_graph(i))
args.max_prev_node = 10
elif args.graph_type=='ladder_small':
graphs = []
for i in range(2, 11):
graphs.append(nx.ladder_graph(i))
args.max_prev_node = 10
elif args.graph_type=='tree':
graphs = []
for i in range(2,5):
for j in range(3,5):
graphs.append(nx.balanced_tree(i,j))
args.max_prev_node = 256
elif args.graph_type=='caveman':
# graphs = []
# for i in range(5,10):
# for j in range(5,25):
# for k in range(5):
# graphs.append(nx.relaxed_caveman_graph(i, j, p=0.1))
graphs = []
for i in range(2, 3):
for j in range(30, 81):
for k in range(10):
graphs.append(caveman_special(i,j, p_edge=0.3))
args.max_prev_node = 100
elif args.graph_type=='caveman_small':
# graphs = []
# for i in range(2,5):
# for j in range(2,6):
# for k in range(10):
# graphs.append(nx.relaxed_caveman_graph(i, j, p=0.1))
graphs = []
for i in range(2, 3):
for j in range(6, 11):
for k in range(20):
graphs.append(caveman_special(i, j, p_edge=0.8)) # default 0.8
args.max_prev_node = 20
elif args.graph_type=='caveman_small_single':
# graphs = []
# for i in range(2,5):
# for j in range(2,6):
# for k in range(10):
# graphs.append(nx.relaxed_caveman_graph(i, j, p=0.1))
graphs = []
for i in range(2, 3):
for j in range(8, 9):
for k in range(100):
graphs.append(caveman_special(i, j, p_edge=0.5))
args.max_prev_node = 20
elif args.graph_type.startswith('community'):
num_communities = int(args.graph_type[-1])
print('Creating dataset with ', num_communities, ' communities')
c_sizes = np.random.choice([12, 13, 14, 15, 16, 17], num_communities)
#c_sizes = [15] * num_communities
for k in range(3000):
graphs.append(n_community(c_sizes, p_inter=0.01))
args.max_prev_node = 80
elif args.graph_type=='grid':
graphs = []
for i in range(10,20):
for j in range(10,20):
graphs.append(nx.grid_2d_graph(i,j))
args.max_prev_node = 40
elif args.graph_type=='grid_small':
graphs = []
for i in range(2,5):
for j in range(2,6):
graphs.append(nx.grid_2d_graph(i,j))
args.max_prev_node = 15
elif args.graph_type=='barabasi':
graphs = []
for i in range(100,200):
for j in range(4,5):
for k in range(5):
graphs.append(nx.barabasi_albert_graph(i,j))
args.max_prev_node = 130
elif args.graph_type=='barabasi_small':
graphs = []
for i in range(4,21):
for j in range(3,4):
for k in range(10):
graphs.append(nx.barabasi_albert_graph(i,j))
args.max_prev_node = 20
elif args.graph_type=='grid_big':
graphs = []
for i in range(36, 46):
for j in range(36, 46):
graphs.append(nx.grid_2d_graph(i, j))
args.max_prev_node = 90
elif 'barabasi_noise' in args.graph_type:
graphs = []
for i in range(100,101):
for j in range(4,5):
for k in range(500):
graphs.append(nx.barabasi_albert_graph(i,j))
graphs = perturb_new(graphs,p=args.noise/10.0)
args.max_prev_node = 99
# real graphs
elif args.graph_type == 'enzymes':
graphs= Graph_load_batch(min_num_nodes=10, name='ENZYMES')
args.max_prev_node = 25
elif args.graph_type == 'enzymes_small':
graphs_raw = Graph_load_batch(min_num_nodes=10, name='ENZYMES')
graphs = []
for G in graphs_raw:
if G.number_of_nodes()<=20:
graphs.append(G)
args.max_prev_node = 15
elif args.graph_type == 'protein':
graphs = Graph_load_batch(min_num_nodes=20, name='PROTEINS_full')
args.max_prev_node = 80
elif args.graph_type == 'DD':
graphs = Graph_load_batch(min_num_nodes=100, max_num_nodes=500, name='DD',node_attributes=False,graph_labels=True)
args.max_prev_node = 230
elif args.graph_type == 'citeseer':
_, _, G = Graph_load(dataset='citeseer')
G = max(nx.connected_component_subgraphs(G), key=len)
G = nx.convert_node_labels_to_integers(G)
graphs = []
for i in range(G.number_of_nodes()):
G_ego = nx.ego_graph(G, i, radius=3)
if G_ego.number_of_nodes() >= 50 and (G_ego.number_of_nodes() <= 400):
graphs.append(G_ego)
args.max_prev_node = 250
elif args.graph_type == 'citeseer_small':
_, _, G = Graph_load(dataset='citeseer')
G = max(nx.connected_component_subgraphs(G), key=len)
G = nx.convert_node_labels_to_integers(G)
graphs = []
for i in range(G.number_of_nodes()):
G_ego = nx.ego_graph(G, i, radius=1)
if (G_ego.number_of_nodes() >= 4) and (G_ego.number_of_nodes() <= 20):
graphs.append(G_ego)
shuffle(graphs)
graphs = graphs[0:200]
args.max_prev_node = 15
elif args.graph_type == 'generated':
graphs = []
file_list = glob.glob('./generated_graphs/*.pkl')
file_list = [s for s in file_list
if args.generated_name in s
and
args.generated_size in s]
print('File_list:\n{}'.format(file_list))
for file in file_list:
A = pkl.load(open(file, 'rb'), encoding='latin-1')
for graph in A['data']:
G = nx.from_numpy_matrix(graph)
graphs.append(G)
args.max_prev_node = 63
return graphs
def gen_BA_graph(n, k):
""" generate one node at a time,
connecting preferentially to higher degree nodes """
return nx.barabasi_albert_graph(n, k)
B.add_edge(u, v, weight=w[weight])
return B
else:
B = nx.Graph() #Undirected case:
for u, v, w in G.edges(data=True):
try:
alpha = w['alpha']
except KeyError: #there is no alpha, so we assign 1. It will never pass the cut
alpha = 1
if alpha < alpha_t:
B.add_edge(u, v, weight=w[weight])
return B
if __name__ == '__main__':
G = nx.barabasi_albert_graph(1000, 5)
for u, v in G.edges():
G[u][v]['weight'] = np.random.randint(1, 100)
alpha = 0.05
G = disparity_filter(G)
G2 = nx.Graph([(u, v, d) for u, v, d in G.edges(data=True)
if d['alpha'] < alpha])
print 'alpha = %s' % alpha
print 'original: nodes = %s, edges = %s' % (G.number_of_nodes(),
G.number_of_edges())
print 'backbone: nodes = %s, edges = %s' % (G2.number_of_nodes(),
G2.number_of_edges())
print G2.edges(data=True)
def test(n=1, num_nodes=500000, debug=True, eid=0, tid=0, gid=0):
global cluster_nodes, node_clusters, g
if not g:
g = nx.barabasi_albert_graph(num_nodes, 5)
#g = nx.erdos_renyi_graph(1000000, .1)
#g = nx.random_lobster(100000, .4, .4)
#g = nx.random_powerlaw_tree(10000)
#g = nx.powerlaw_cluster_graph(100000, 10, .2)
#print str(g.adj)
#nx.draw(g)
#plt.show()
#num_landmarks = 20
print "Running Louvain algorithm"
cluster_nodes, node_clusters = louvain_clustering(g)
#print "Cluster Nodes:", str(len(node_clusters))
#ALP will have this many landmarks as well
num_landmarks = len(cluster_nodes.keys())
print "Cluster Nodes:", str(num_landmarks)
first = True
import calendar
#time since epoch
print "Running queries..."
cur_time = calendar.timegm(time.gmtime())
sys.stdout.flush()
for i in range(n):
num_alp_calculations = 0
num_alp_estimates = 0
s = choice(g.nodes())
t = choice(g.nodes())
if debug:
print "S:", str(s)
print "T:", str(t)
if debug:
print "Running A* with no heuristic (Dijkstra's)"
start = time.time()
path, astar_size = pp.astar_path(g,s,t, search_space_size=True)
if debug:
print "A* (no heuristic): ", str(path), "Size:", astar_size
end = time.time()
empty_a_star_time = end - start
#print str(end - start), "seconds."
num_alp_calculations = 0
num_alp_estimates = 0
start = time.time()
path, alp_size = pp.astar_path(g,s,t,ALP, search_space_size=True)
if debug:
print "A* (ALP)): ", str(path), "Size:", alp_size, "Number of ALP calculations:", str(num_alp_calculations), "Numbber of ALP Estimates:", str(num_alp_estimates)
end = time.time()
alp_time = end - start
#print str(end - start), "seconds."
num_alt_calculations = 0
num_alt_estimates = 0
start = time.time()
path, alt_size = pp.astar_path(g,s,t,ALT, search_space_size=True)
if debug:
print "A* (ALT)): ", str(path), "Size:", alt_size, "Number of ALT calculations:", str(num_alt_calculations), "Numbber of ALT Estimates:", str(num_alt_estimates)
end = time.time()
alt_time = end - start
#print str(end - start), "seconds."
if debug:
print(str(empty_a_star_time) + ',' + str(astar_size) + ',' + str(alt_time) + ',' + str(alt_size) + ',' + str(num_alt_calculations) + ',' + str(num_alt_estimates) + ',' + str(alp_time) + ',' + str(alp_size) + ',' + str(num_alp_calculations) + ',' + str(num_alp_estimates) + ',' + str(len(path)) + ',' + str(num_landmarks) + '\n')
if first:
first = False
else:
a_star_sql = "(NULL," + str(tid) + "," + str(heuristics.DIJKSTRA) + ", NULL," + str(s) + "," + str(t) + "," + str(len(path)) + ", NULL," + str(empty_a_star_time) + "," + str(astar_size) + ",0,0)"
alt_sql = "(NULL," + str(tid) + "," + str(heuristics.ALT_OPT) + "," + str(embedding_methods.RANDOM) + "," + str(s) + "," + str(t) + "," + str(len(path)) + "," + str(len(landmarks)) + "," + str(alt_time) + "," + str(alt_size) + "," + str(num_alt_calculations) + "," + str(num_alt_estimates) + ")"
alp_sql = "(NULL," + str(tid) + "," + str(heuristics.ALP_OPT) + "," + str(embedding_methods.RANDOM) + "," + str(s) + "," + str(t) + "," + str(len(path)) + "," + str(num_landmarks) + "," + str(alp_time) + "," + str(alp_size) + "," + str(num_alp_calculations) + "," + str(num_alp_estimates) + ")"
query_sql = "INSERT INTO query VALUES " + a_star_sql + ", " + alt_sql + ", " + alp_sql
if debug:
print "QUERY SQL:", query_sql
dba.query_insert(query_sql)
#insert trial
tend_time = time.time()
#trial has ended. wipe the graph
#start with ALT
for v in g.nodes():
for l in landmarks:
if 'ALT_'+str(l) in g.node[v]:
del g.node[v]['ALT_'+str(l)]
#now ALP
for l in alp_landmarks:
for v in [n for n in g.nodes() if 'ALP_'+str(l) in g.node[n]]:
del g.node[v]['ALP_' + str(l)]
for i in range(number_of_seeds)]
winner_change_graph_avg = [0 for j in range(xLengthGraph)]
numOfIteration = 0
for seedi in range(number_of_seeds):
seede = 363 + seedi * 140
random.seed(seede)
print("######################### next seed , the seed is ", seede,
"#################")
for a1 in range(xLengthGraph):
Clean(winnerVotes)
num_of_Friends = 300 + (50 * a1) #300-800 friends
barabasiGraph = barabasi_albert_graph(num_of_Friends,
num_of_Arcs,
seed=seede)
# מתחיל לייצר קשתות רק כאשר יש לו אמ קודקודים ברשת
# (n-m)m = edge
edges = nx.edges(barabasiGraph)
# print(edges)
friends = [set() for j in range(len(barabasiGraph))]
# [(0, 2), (0, 3), (0, 1), (1, 2), (1, 3), (1, 4), (2, 3), (2, 4)]
Opinions = creatOpinions(barabasiGraph, typeOfVotes, Opinions2,
winnerVotes)
# print(np.matrix(Opinions))
winnerStart = getWinner(winnerVotes, typeOfVotes)
pylab.rcParams['xtick.major.pad'] = '8'
pylab.rcParams['ytick.major.pad'] = '8'
Trans = 5000
nNodes = int(os.getenv('Nodes'))
initial_nodes = int(os.getenv('Initial_Nodes'))
Which_network = os.getenv('Network')
Mean1 = []
ColorMap = []
if (Which_network == 'BARABASI'):
Mean = []
in_Degree = []
for Seed in range(1, 51):
path = 'Output_Scale_free_1_init_node/Output_Network_Seed_%04i/Output_0001/Output_alpha_20.00/Output_beta_30.00/' % Seed
G = nx.barabasi_albert_graph(nNodes, initial_nodes, seed=Seed)
H = G.to_directed()
A = np.loadtxt('%s/Data_0155.dat' % path, unpack=True)
C = np.loadtxt('%s/List_Reg_not_sorted.dat' % path, unpack=True)
C.view('f8,f8,f8').sort(order=['f0'], axis=-1)
for i in range(1, nNodes + 1):
Mean.append(A[i][Trans:].mean())
for i in range(nNodes):
in_Degree.append(H.in_degree(i))
ColorMap.append(C[2][i])
fig = matplotlib.pyplot.gcf()
axes = fig.add_subplot(111)
fig.set_size_inches(14.5, 10.5)
tree_prior = TreePrior(n)
prior = Prior(n, Param, alpha=.5, tree_prior=tree_prior)
g = Param(n)
n_edges = n * (n - 1) // 2
while g.EdgeCount() <= int(.1 * n_edges) or g.EdgeCount() > int(.4 * n_edges):
g = prior.Sample()
graphs.append(g.copy())
g = Param(n)
while g.EdgeCount() <= int(.6 * n_edges) or g.EdgeCount() > int(.9 * n_edges):
g = prior.Sample()
graphs.append(g.copy())
# Random Graph
### change this to only graph in the cycle space
g = Param(n, nx.to_dict_of_lists(nx.erdos_renyi_graph(n, .2, seed=123)))
graphs.append(g.copy())
g = Param(n, nx.to_dict_of_lists(nx.barabasi_albert_graph(n, 2, seed=123)))
graphs.append(g.copy())
names = ["empty", "circle", "random0", "random1", "random2", "random3"]
assert(len(names) == len(graphs))
for i in range(len(names)):
generate_data_and_save(n, n_obs, graphs[i], f"data/{names[i]}_{n}_{n_obs}.dat", threshold=None, seed=123)
import pickle
for i in range(len(names)):
with open(f"data/graph_{names[i]}_{n}_{n_obs}.pkl", 'wb') as handle:
pickle.dump(graphs[i], handle)
def simulate_series(simulation_data):
disease_params = simulation_data
#disease_params['order'] = 1
simulation_data['G'] = simulation_data['network_n'] # genes
simulation_data['S'] = simulation_data['P']
simulation_data['patients_number'] = 2 * simulation_data['S']
# make network
if (simulation_data['network_type'] == 'BA'):
g = nx.barabasi_albert_graph(simulation_data['network_n'],
simulation_data['network_m'])
elif (simulation_data['network_type'] == 'ER'):
g = nx.erdos_renyi_graph(simulation_data['network_n'],
simulation_data['network_p'])
elif (simulation_data['model'] == '2D'):
g = nx.grid_2d_graph(simulation_data['network_x'],
simulation_data['network_y'],
periodic=True)
elif (simulation_data['model'] == 'CYC'):
g = nx.cycle_graph(simulation_data['network_n'])
elif (simulation_data['model'] == 'REG'):
g = nx.random_regular_graph(simulation_data['network_d'],
simulation_data['network_n'])
#neighbors_dict = all_neighbors_order(g, simulation_params['order'])
colored_graph = color_nodes_order(g, disease_params['D'],
disease_params['p'],
disease_params['order'])
#colored_graph = color_nodes_order(g, disease_params['D'], disease_params['p'], disease_params['order'])
g_strip = g.copy()
#solitary = [ n for n,d in g_strip.degree_iter() if d == 0 ]
solitary = [n for n, d in g_strip.degree() if d == 0]
g_strip.remove_nodes_from(solitary)
layout = nx.spring_layout(g_strip)
result = {}
#result['layout'] = layout
#result['g'] = g
#result['g_strip'] = g_strip
for disease_params['rho_0'] in disease_params['rho_0_list']:
result[str(disease_params['rho_0'])] = {}
result_disease = simulate_artificial_disease(disease_params,
simulation_data,
colored_graph,
colored_graph)
collective_genes = get_collective_gene_expression(
simulation_data,
result_disease['expressed_genes_under'],
result_disease['expressed_genes_over'],
result_disease['phenotype_table'],
mode='normal')
filtered = collective_genes['flt']
for flt in simulation_data['f_list']:
#result[str(disease_params['rho_0'])][str(flt)] = {}
tmp_result = {}
tmp_result['expressed_genes_sick'] = result_disease[
'expressed_genes_sick']
tmp_result['expressed_genes_healthy'] = result_disease[
'expressed_genes_healthy']
tmp_result['extracted_genes'] = list(
set(filtered['dis_over_flt_' + str(flt)]))
tmp_result['disease_genes'] = list(
set(filtered['dis_under_flt_' + str(flt)]))
tmp_result['true_poositive_genes'] = list(
set(filtered['dis_under_flt_' + str(flt)])
& set(filtered['dis_over_flt_' + str(flt)]))
tmp_result['disease_params'] = disease_params
tmp_result['layout'] = layout
tmp_result['g'] = g
tmp_result['g_strip'] = g_strip
tmp_result['rho_0'] = disease_params['rho_0']
tmp_result['flt'] = flt
result[str(disease_params['rho_0'])][str(flt)] = tmp_result
return result
# Prints out statistics of real network
info(G)
# Plots degree distribution of real network
plot(G)
# Prints out statistics of Erdös-Rényi random graph
ER = nx.gnm_random_graph(G.number_of_nodes(), G.number_of_edges())
ER.name = 'Erdös-Rényi'
info(ER)
# Prints out statistics of Barabási–Albert scale-free graph
BA = nx.barabasi_albert_graph(
G.number_of_nodes(), round(G.number_of_edges() / G.number_of_nodes()))
BA.name = 'Barabási–Albert'
info(BA)
# Prints out statistics of Watts–Strogatz small-world graph
WS = nx.watts_strogatz_graph(
G.number_of_nodes(),
round(2.0 * G.number_of_edges() / G.number_of_nodes()), 0.05)
WS.name = 'Watts–Strogatz'
info(WS)
print("{0:>15s} | {1:.1f} sec\n".format('Total', time() - tic))
for graph_type in range(12, 21):
if first:
sizes = [175000, 300000]
first = False
else:
sizes = [500, 2500, 12500, 60000, 175000, 300000]
first = False
try:
#choose different graph size (x2)
for num_n in sizes:
#choose different graph
g_name = "NULL"
print "Forming graph of size ", str(num_n)
print str(graph_type)
if graph_type == 0:
g = nx.barabasi_albert_graph(num_n, 2)
g_name = 'NETWORKX.BARABASI_ALBERT_2'
elif graph_type == 1:
g = nx.barabasi_albert_graph(num_n, 3)
g_name = 'NETWORKX.BARABASI_ALBERT_3'
elif graph_type == 2:
g = nx.barabasi_albert_graph(num_n, 5)
g_name = 'NETWORKX.BARABASI_ALBERT_5'
elif graph_type == 3:
g = nx.barabasi_albert_graph(num_n, 7)
g_name = 'NETWORKX.BARABASI_ALBERT_7'
elif graph_type == 4:
g = nx.barabasi_albert_graph(num_n, 9)
g_name = 'NETWORKX.BARABASI_ALBERT_9'
elif graph_type == 5:
g = nx.barabasi_albert_graph(num_n, 11)
#!/usr/bin/env python import networkx as nx #nx.write_edgelist(nx.DiGraph(nx.complete_graph(100)), 'complete-small.txt', data=False) #nx.write_edgelist(nx.DiGraph(nx.barabasi_albert_graph(100, 3)), 'social-small.txt', data=False) #nx.write_edgelist(nx.DiGraph(nx.barabasi_albert_graph(200000, 3)), 'social-large-200k.txt', data=False) #nx.write_edgelist(nx.DiGraph(nx.barabasi_albert_graph(400000, 3)), 'social-large-400k.txt', data=False) nx.write_edgelist(nx.DiGraph(nx.barabasi_albert_graph(800000, 3)), 'social-large-800k.txt', data=False) #nx.write_edgelist(nx.DiGraph(nx.barabasi_albert_graph(1600000, 3)), 'social-large-1600k.txt', data=False) #nx.write_edgelist(nx.DiGraph(nx.barabasi_albert_graph(3200000, 3)), 'social-large-3200k.txt', data=False)
from sklearn.preprocessing import Normalizer, MinMaxScaler
from scipy.sparse import csgraph
import scipy
import os
from sklearn import datasets
os.chdir('Documents/research/graph_bandit/code/')
from bandit_model import *
from utils import *
os.chdir('Documents/research/graph_bandit/results/')
node_num = 10
dimension = 5
step_size = 0.001
iteration = 100
pos = np.random.normal(size=(node_num, 2))
graph = nx.barabasi_albert_graph(node_num, 5)
adj = np.array(
nx.to_numpy_matrix(graph)) * np.random.uniform(size=(node_num, node_num))
lap = csgraph.laplacian(adj, normed=False)
user_f = np.random.random(size=(node_num, dimension))
adj = rbf_kernel(user_f)
np.fill_diagonal(adj, 0)
lap = csgraph.laplacian(adj, normed=False)
def generate_signal(lap, s_t_1):
p_t = np.random.normal(size=1, scale=1)
s_t = np.dot(np.exp(-lap), s_t_1) + p_t
return s_t
