def synth_plots():
num_nodes = 100
samples = 5
chunglu_M = []
kron_M = []
HRG_M = []
pHRG_M = []
G_M = []
for i in range(0, samples):
##BA Graph
G = nx.erdos_renyi_graph(num_nodes, .1)
G_M.append(G)
for i in range(0, samples):
chunglu_M.append(nx.expected_degree_graph(G.degree().values()))
HRG_M_s, degree = HRG.stochastic_hrg(G, samples)
HRG_M = HRG_M + HRG_M_s
pHRG_M_s = PHRG.probabilistic_hrg(G, samples)
pHRG_M = pHRG_M + pHRG_M_s
for i in range(0, samples):
P = kronfit(G)
k = math.log(num_nodes, 2)
kron_M.append(
product.kronecker_random_graph(int(math.floor(k)),
P,
directed=False))
metrics.draw_network_value(G_M, chunglu_M, HRG_M, pHRG_M, kron_M)
def KPGM(G, gname):
target_nodes = G.number_of_nodes()
k = int(math.log(target_nodes, 2))
print 'k=', k
# from: i:/data/saguinag/Phoenix/demo_graphs/karate.txt
# karate: P = [[0.9999,0.661],[0.661, 0.01491]]
# Interent: autonomous systems
# P = [[0.9523, 0.585],[0.585, 0.05644]]
P = kronfit(G)
#print 'kronfit params (matrix):', P
pred_graph = product.kronecker_random_graph(k, P)
for u, v in pred_graph.selfloop_edges():
pred_graph.remove_edge(u, v)
return pred_graph
def get_kpgm_graph_recurrence(graph,recurrence_nbr=1): """ Returns ------- nth graph --<kpgm>-- """ import product import math from cikm_experiments import kronfit for r in range(recurrence_nbr): k = int(math.log(graph.number_of_nodes(),2)) P = kronfit(graph) #[[0.9999,0.661],[0.661, 0.01491]] KPG = product.kronecker_random_graph(k, P, directed=False) for u,v in KPG.selfloop_edges(): KPG.remove_edge(u, v) graph = KPG if KPG.is_directed(): print '!!!!!! is directed' print '\tKronecker -> run:', r, graph.number_of_nodes(),graph.number_of_edges() return KPG
def grow_graphs_using_kpgm(graph,recurrence_nbr=1): """ Returns ------- nth graph --<kpgm>-- """ import product import math from pami import kronfit kp_graphs = [] k = int(math.log(graph.number_of_nodes(),2)) P = kronfit(graph) #[[0.9999,0.661],[0.661, 0.01491]] print P, 'your are here'*2 exit() for r in range(recurrence_nbr): KPG = product.kronecker_random_graph(k, P) for u,v in KPG.selfloop_edges(): KPG.remove_edge(u, v) graph = KPG print '\tKronecker -> run:', r, graph.number_of_nodes(),graph.number_of_edges() kp_graphs.append(graph) return kp_graphs
pred_graph = grow(rule_list, g)[0]
if model == 'chlu': # ChungLu
z = graphical_degree_sequence(target_nodes)
pred_graph = nx.expected_degree_graph(z)
if model == 'kpgm': # KPGM -
k = int(math.log(target_nodes, 2))
# from: i:/data/saguinag/Phoenix/demo_graphs/karate.txt
# karate: P = [[0.9999,0.661],[0.661, 0.01491]]
# Interent: autonomous systems
# P = [[0.9523, 0.585],[0.585, 0.05644]]
P = kronfit(G)
print P
pred_graph = product.kronecker_random_graph(k, P)
for u, v in pred_graph.selfloop_edges():
pred_graph.remove_edge(u, v)
ofname = "../Results/{}_{}_{}.gpickle".format(gname, model, i)
nx.write_gpickle(pred_graph, ofname)
print ofname
G = pred_graph
print 'Done'
# write this graph to tmp.edglst to fit Kron params
# nx.write_edgelist(G,'tmp.edglst')
# multiGraphs.append(hstar)
# chungluGraphs.append(cl_grph)
# # kronGraphs.append(KPG)
import product
import networkx as nx
from PIL import Image, ImageDraw
import random
k = 6
P = [[1,1,0],[0,1,0],[0,1,.98]]
G = product.kronecker_random_graph(k,P)
for u,v in G.selfloop_edges():
G.remove_edge(u,v)
pr = nx.pagerank(G)
maxPR = max(pr.values())
filename = 'kronecker'
size = 1000
rectWidth = 10
im = Image.new("RGB", (size, size), "white")
draw = ImageDraw.Draw(im)
for edge in G.edges():
x = (1 - (pr[edge[0]] / maxPR)) * (size - rectWidth - rectWidth) + rectWidth
y = (1 - (pr[edge[1]] / maxPR)) * (size - rectWidth - rectWidth) + rectWidth
draw.rectangle([x, y, x + rectWidth, y + rectWidth], fill='black')
draw.rectangle([y, x, y + rectWidth, x + rectWidth], fill='black')
im.save('./fig/' + filename + '.scale.png')
nodes = G.nodes()
x = sorted(pr.values(), key = lambda node: node, reverse = True)
def gcd():
num_nodes = 1000
ba_G = nx.barabasi_albert_graph(num_nodes, 3)
er_G = nx.erdos_renyi_graph(num_nodes, .1)
ws_G = nx.watts_strogatz_graph(num_nodes, 8, .1)
nws_G = nx.newman_watts_strogatz_graph(num_nodes, 8, .1)
graphs = [ba_G, er_G, ws_G, nws_G]
samples = 50
for G in graphs:
chunglu_M = []
for i in range(0, samples):
chunglu_M.append(nx.expected_degree_graph(G.degree()))
HRG_M, degree = HRG.stochastic_hrg(G, samples)
pHRG_M = PHRG.probabilistic_hrg(G, samples)
kron_M = []
rmat_M = []
for i in range(0, samples):
P = kronfit(G)
k = math.log(num_nodes, 2)
kron_M.append(
product.kronecker_random_graph(int(math.floor(k)),
P,
directed=False))
df_g = metrics.external_rage(G)
gcd_chunglu = []
gcd_phrg = []
gcd_hrg = []
gcd_kron = []
for chunglu_M_s in chunglu_M:
df_chunglu = metrics.external_rage(chunglu_M_s)
rgfd = metrics.tijana_eval_rgfd(df_g, df_chunglu)
gcm_g = metrics.tijana_eval_compute_gcm(df_g)
gcm_h = metrics.tijana_eval_compute_gcm(df_chunglu)
gcd_chunglu.append(metrics.tijana_eval_compute_gcd(gcm_g, gcm_h))
for HRG_M_s in HRG_M:
df_hrg = metrics.external_rage(HRG_M_s)
rgfd = metrics.tijana_eval_rgfd(df_g, df_hrg)
gcm_g = metrics.tijana_eval_compute_gcm(df_g)
gcm_h = metrics.tijana_eval_compute_gcm(df_hrg)
gcd_hrg.append(metrics.tijana_eval_compute_gcd(gcm_g, gcm_h))
for pHRG_M_s in pHRG_M:
df_phrg = metrics.external_rage(pHRG_M_s)
rgfd = metrics.tijana_eval_rgfd(df_g, df_phrg)
gcm_g = metrics.tijana_eval_compute_gcm(df_g)
gcm_h = metrics.tijana_eval_compute_gcm(df_phrg)
gcd_phrg.append(metrics.tijana_eval_compute_gcd(gcm_g, gcm_h))
for kron_M_s in kron_M:
df_kron = metrics.external_rage(kron_M_s)
rgfd = metrics.tijana_eval_rgfd(df_g, df_kron)
gcm_g = metrics.tijana_eval_compute_gcm(df_g)
gcm_h = metrics.tijana_eval_compute_gcm(df_kron)
gcd_kron.append(metrics.tijana_eval_compute_gcd(gcm_g, gcm_h))
print gcd_chunglu
print gcd_hrg
print gcd_phrg
print gcd_kron
print
print
if (len(c) is not 3): continue
E.add_edge(int(c[1]), int(c[2]))
if int(c[1]) > num_nodes or int(c[2]) > num_nodes:
continue
Gergm.append(E)
print "G ergm iteration " + str(run) + " of 20"
Gergmgl.append(gs.subgraphs_cnt(E, 50))
k = int(math.floor(math.log(num_nodes, 10)))
P = [[.9716, .658], [.5684, .1256]] #karate
P = [[.8581, .5116], [.5063, .2071]] #as20000102
#P = [[.7317,.5533],[.5354,.2857]] #dblp
#P = [[.9031,.5793],[.5051,.2136]] #ca-grqc
#P = [[.9124,.5884],[.5029,.2165]] #enron
P = [[.8884, .5908], [.5628, .2736]] #brightkite
Gkron = product.kronecker_random_graph(k, P).to_undirected()
print("GKron finished")
sum = .9716 + .5382 + .5684 + .1256 #karate
#sum = .8581+.5116+.5063+.2071 #as20000102
#sum = .7317+.5533+.5354+.2857 # dblp
#sum = .9031+.5793+.5051+.2136 #ca-grqc
#sum = .9124+.5884+.5029+.2165 #enron
sum = .8884 + .5908 + .5628 + .2736 #brightkite
GRmatSNAP = snap.GenRMat(num_nodes, num_edges, P[0][0] / sum, P[0][1] / sum,
P[1][0] / sum)
GRmat = nx.Graph()
for EI in GRmatSNAP.Edges():
GRmat.add_edge(EI.GetSrcNId(), EI.GetDstNId())