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sinfluence.py
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sinfluence.py
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__author__ = "Moses A. Boudourides & Sergios T. Lenis"
__copyright__ = "Copyright (C) 2015 Moses A. Boudourides & Sergios T. Lenis"
__license__ = "Public Domain"
__version__ = "1.0"
'''
This script computes and plots simulations of network infuence.
'''
import networkx as nx
import utils_attributes as utilat
import matplotlib.pyplot as plt
import matplotlib.colors as colors
import itertools as it
import numpy as np
import pygraphviz
from networkx.drawing.nx_agraph import graphviz_layout
np.seterr(all='ignore')
def influence_sim(G,pos,sa,iterations,scale=1000): #nodes,p,
# while True:
# # G=nx.connected_watts_strogatz_graph(25, 2, 0.8, tries=100)
# G=nx.erdos_renyi_graph(nodes,p)
# if nx.is_connected(G):
# break
# G.remove_nodes_from(nx.isolates(G))
# # col=y
# pos0=graphviz_layout(G) #nx.spring_layout(G)
# pos=pos0 #nx.spring_layout(G)
##nx.draw_networkx(g,pos=pos, node_color=col,cmap=plot.cm.Reds)
# scale=1000
F,asoc=utilat.create_random_scalar_attributes(G,scale)
col=[F.node[i]['scalar_attribute'] for i in F.nodes()]
fig = plt.figure(figsize=(17,17))
sstt="Initial distribution of the opinions over graph nodes" #\n (diffusion source at node %s with initial attribute =%f)" %(F.nodes()[0],starting_value_of_zero_node)
plt.subplot(3,2,1).set_title(sstt)
# plt.set_cmap('cool')
nx.draw_networkx(G=G,pos=pos, node_color=col,vmin=0.,vmax=1.)
plt.axis('equal')
plt.axis('off')
# plt.figure(2)
sstt = "Time variation of opinions over graph nodes" #%F.nodes()[0]#,starting_value_of_zero_node)
plt.subplot(3,2,5).set_title(sstt)
# for i in F.nodes(data=True):
# print i
# print nx.numeric_assortativity_coefficient(F,'scalar_attribute_numeric')
iterat=[]
assort=[]
y=[F.node[i]['scalar_attribute'] for i in F.nodes()]
ckck=4
kckc=2
for ii in range(iterations):
# sa=0.05
checkin=True
nd=list(F.nodes())[0]
# print nd
uu=0
nei=list(F.neighbors(nd))
# print nei
for nnei in nei:
uu+=F.node[nnei]['scalar_attribute']
Xnei=uu/len(nei)
X=F.node[nd]['scalar_attribute']
uX=(sa*Xnei)+(1-sa)*X
insau=int(uX*scale)
F.add_node(nd,scalar_attribute=uX,scalar_attribute_numeric=insau)
for nd in list(F.nodes())[1:]:
# sa=1-(1./nx.degree(F,nd))
uu=0
nei=list(nx.neighbors(F,nd))
# print nei
for nnei in nei:
uu+=F.node[nnei]['scalar_attribute']
X=F.node[nd]['scalar_attribute']
Xnei=uu/len(nei)
uX=(sa*Xnei)+(1-sa)*X
insau=int(uX*scale)
F.add_node(nd,scalar_attribute=uX,scalar_attribute_numeric=insau)
# for i in F.nodes(data=True):
# print i
# Checking for attributes equality
y1=y
y=[F.node[i]['scalar_attribute'] for i in F.nodes()]
if ii ==ckck and ii<10:
# col=['%.5f' %yy for yy in y]
col=[F.node[i]['scalar_attribute'] for i in F.nodes()]
sstt="Distribution of the opinions over graph nodes at %i iterations" %(ii+1)
plt.subplot(3,2,kckc).set_title(sstt)
nx.draw_networkx(G=G,pos=pos, node_color=col,vmin=0.,vmax=1.)
plt.axis('equal')
plt.axis('off')
ckck+=5
kckc+=1
plt.subplot(3,2,5)
# for yy in it.combinations(y,2):
# checkin=checkin and yy[0] -yy[1] <(1./scale)
# for yy in range(len(y1)):
# # print y[yy]-y1[yy]<(1./scale)
# checkin=checkin and y[yy]-y1[yy]<(0.1/(scale))
# # print checkin
# if checkin:
# break
asss=nx.numeric_assortativity_coefficient(F,'scalar_attribute_numeric')
if np.isnan(asss):
asss=1.
# print 'Iteration %i ==> %f' %(ii,asss),y
# print type(asss), asss<-1,asss>1
iterat.append(ii)
assort.append(asss)
# print nx.numeric_assortativity_coefficient(F,'scalar_attribute_numeric')
# y=[F.node[i]['scalar_attribute'] for i in F.nodes()]
plt.plot(F.nodes(),y)
# if asss==1. :
# break
plt.plot(F.nodes(),y,linewidth=3.)
sstt= "Time variation of the opinion assortativity coefficient"
plt.subplot(3,2,6).set_title(sstt)
# plt.figure(3)
plt.plot(iterat,assort)
plt.ylim(-1.1,1.1)
# plt.figure(3)
# plt.plot(F.nodes(),y)
# col=['%.5f' %yy for yy in y]
col=[F.node[i]['scalar_attribute'] for i in F.nodes()]
sstt="Distribution of the opinions over graph nodes at %i iterations\n (consensual attribute = %s)" %(iterations,col[0])
plt.subplot(3,2,4).set_title(sstt)
# plt.figure(4)
# print col
# print [F.node[i]['scalar_attribute_numeric'] for i in F.nodes()]
# pos=nx.spring_layout(G)
##nx.draw_networkx(g,pos=pos, node_color=col,cmap=plot.cm.Reds)
nx.draw_networkx(G=G,pos=pos, node_color=col,vmin=0.,vmax=1.)
plt.axis('equal')
plt.axis('off')
plt.show()
# plt.subplot(2,2,1).set_title("Original distribution of the opinions over graph nodes")
# # plt.set_cmap('cool')
# nx.draw_networkx(G,pos=pos, node_color=col,vmin=0.,vmax=1.)
# # nx.draw_networkx(G,pos=pos, node_color=col,cmap=plt.cm.Cool)
# plt.axis('equal')
# plt.axis('off')
# # plt.figure(2)
# plt.subplot(2,2,2).set_title("Time variation of opinions over graph nodes")
# # for i in F.nodes(data=True):
# # print i
# # print nx.numeric_assortativity_coefficient(F,'scalar_attribute_numeric')
# iterat=[]
# assort=[]
# y=[F.node[i]['scalar_attribute'] for i in F.nodes()]
# for ii in range(iterations):
# # sa=0.05
# checkin=True
# for nd in F.nodes():
# # sa=1-(1./nx.degree(F,nd))
# uu=0
# nei=nx.neighbors(F,nd)
# # print nei
# for nnei in nei:
# uu+=F.node[nnei]['scalar_attribute']
# sau=(sa*uu/len(nei))+(1-sa)*F.node[nd]['scalar_attribute']
# insau=int(sau*scale)
# F.add_node(nd,scalar_attribute=sau,scalar_attribute_numeric=insau)
# # for i in F.nodes(data=True):
# # print i
# # Checking for attributes equality
# y1=y
# y=[F.node[i]['scalar_attribute'] for i in F.nodes()]
# # for yy in it.combinations(y,2):
# # checkin=checkin and yy[0] -yy[1] <(1./scale)
# # for yy in range(len(y1)):
# # # print y[yy]-y1[yy]<(1./scale)
# # checkin=checkin and y[yy]-y1[yy]<(0.1/(scale))
# # # print checkin
# # if checkin:
# # break
# asss=nx.numeric_assortativity_coefficient(F,'scalar_attribute_numeric')
# if np.isnan(asss):
# asss=1.
# # print 'Iteration %i ==> %f' %(ii,asss),y
# # print type(asss), asss<-1,asss>1
# iterat.append(ii)
# assort.append(asss)
# # print nx.numeric_assortativity_coefficient(F,'scalar_attribute_numeric')
# # y=[F.node[i]['scalar_attribute'] for i in F.nodes()]
# plt.plot(F.nodes(),y)
# # if asss==1. :
# # break
# plt.plot(F.nodes(),y,linewidth=3.)
# plt.subplot(2,2,3).set_title("Time variation of the opinion assortativity coefficient")
# # plt.figure(3)
# plt.plot(iterat,assort)
# plt.ylim(-1.1,1.1)
# # plt.figure(3)
# # plt.plot(F.nodes(),y)
# plt.subplot(2,2,4).set_title("Final distribution of the opinions over graph nodes")
# # plt.figure(4)
# col=['%.5f' %yy for yy in y]
# # print col
# # print [F.node[i]['scalar_attribute_numeric'] for i in F.nodes()]
# # pos=nx.spring_layout(G)
# ##nx.draw_networkx(g,pos=pos, node_color=col,cmap=plot.cm.Reds)
# nx.draw_networkx(G,pos=pos, node_color=col)
# plt.axis('equal')
# plt.axis('off')
# plt.show()
def infdif_sim(G,pos,sa,sb,iterations,scale=1000): #nodes,p,
# while True:
# # G=nx.connected_watts_strogatz_graph(25, 2, 0.8, tries=100)
# G=nx.erdos_renyi_graph(nodes,p)
# if nx.is_connected(G):
# break
# G.remove_nodes_from(nx.isolates(G))
# # col=y
# pos0=graphviz_layout(G) #nx.spring_layout(G)
# pos=pos0 #nx.spring_layout(G)
##nx.draw_networkx(g,pos=pos, node_color=col,cmap=plot.cm.Reds)
# scale=1000
F,asoc=utilat.create_random_scalar_attributes(G,scale)
col=[F.node[i]['scalar_attribute'] for i in F.nodes()]
fig = plt.figure(figsize=(17,17))
starting_value_of_zero_node=F.node[0]['scalar_attribute']
sstt="Initial distribution of the opinions over graph nodes\n (diffusion source at node %s with initial attribute = %f)" %(list(F.nodes())[0],starting_value_of_zero_node)
plt.subplot(3,2,1).set_title(sstt)
# plt.set_cmap('cool')
nx.draw_networkx(G=G,pos=pos, node_color=col,vmin=0.,vmax=1.)
plt.axis('equal')
plt.axis('off')
# plt.figure(2)
sstt = "Time variation of opinions over graph nodes" #%F.nodes()[0]#,starting_value_of_zero_node)
plt.subplot(3,2,5).set_title(sstt)
# for i in F.nodes(data=True):
# print i
# print nx.numeric_assortativity_coefficient(F,'scalar_attribute_numeric')
iterat=[]
assort=[]
y=[F.node[i]['scalar_attribute'] for i in F.nodes()]
ckck=4
kckc=2
for ii in range(iterations):
# sa=0.05
checkin=True
nd=list(F.nodes())[0]
# print nd
uu=0
nei=list(nx.neighbors(F,nd))
# print nei
for nnei in nei:
uu+=F.node[nnei]['scalar_attribute']
Xnei=uu/len(nei)
X=F.node[nd]['scalar_attribute']
uX=(sa*Xnei)+(1-sa)*X
insau=int(uX*scale)
F.add_node(nd,scalar_attribute=uX,scalar_attribute_numeric=insau)
for nd in list(F.nodes())[1:]:
# sa=1-(1./nx.degree(F,nd))
uu=0
nei=list(nx.neighbors(F,nd))
# print nei
for nnei in nei:
uu+=F.node[nnei]['scalar_attribute']
Xnei=uu/len(nei)
X=F.node[nd]['scalar_attribute']
uX=(sb*Xnei)+(1-sb)*X
insau=int(uX*scale)
F.add_node(nd,scalar_attribute=uX,scalar_attribute_numeric=insau)
# for i in F.nodes(data=True):
# print i
# Checking for attributes equality
y1=y
y=[F.node[i]['scalar_attribute'] for i in F.nodes()]
if ii ==ckck and ii<10:
# col=['%.5f' %yy for yy in y]
col=[F.node[i]['scalar_attribute'] for i in F.nodes()]
sstt="Distribution of the opinions over graph nodes at %i iterations" %(ii+1)
plt.subplot(3,2,kckc).set_title(sstt)
nx.draw_networkx(G,pos=pos, node_color=col,vmin=0.,vmax=1.)
plt.axis('equal')
plt.axis('off')
ckck+=5
kckc+=1
plt.subplot(3,2,5)
# for yy in it.combinations(y,2):
# checkin=checkin and yy[0] -yy[1] <(1./scale)
# for yy in range(len(y1)):
# # print y[yy]-y1[yy]<(1./scale)
# checkin=checkin and y[yy]-y1[yy]<(0.1/(scale))
# # print checkin
# if checkin:
# break
asss=nx.numeric_assortativity_coefficient(F,'scalar_attribute_numeric')
if np.isnan(asss):
asss=1.
# print 'Iteration %i ==> %f' %(ii,asss),y
# print type(asss), asss<-1,asss>1
iterat.append(ii)
assort.append(asss)
# print nx.numeric_assortativity_coefficient(F,'scalar_attribute_numeric')
# y=[F.node[i]['scalar_attribute'] for i in F.nodes()]
plt.plot(F.nodes(),y)
# if asss==1. :
# break
plt.plot(F.nodes(),y,linewidth=3.)
sstt= "Time variation of the opinion assortativity coefficient"
plt.subplot(3,2,6).set_title(sstt)
# plt.figure(3)
plt.plot(iterat,assort)
plt.ylim(-1.1,1.1)
# plt.figure(3)
# plt.plot(F.nodes(),y)
# col=['%.5f' %yy for yy in y]
col=[F.node[i]['scalar_attribute'] for i in F.nodes()]
sstt="Distribution of the opinions over graph nodes at %i iterations\n (consensual attribute = %s)" %(iterations,col[0])
plt.subplot(3,2,4).set_title(sstt)
# plt.figure(4)
# print col
# print [F.node[i]['scalar_attribute_numeric'] for i in F.nodes()]
# pos=nx.spring_layout(G)
##nx.draw_networkx(g,pos=pos, node_color=col,cmap=plot.cm.Reds)
nx.draw_networkx(G=G,pos=pos, node_color=col,vmin=0.,vmax=1.)
plt.axis('equal')
plt.axis('off')
plt.show()
def polinf_sim(G,pos,iterations,scale=1000): #nodes,p,
# while True:
# # G=nx.connected_watts_strogatz_graph(25, 2, 0.8, tries=100)
# G=nx.erdos_renyi_graph(nodes,p)
# if nx.is_connected(G):
# break
# G.remove_nodes_from(nx.isolates(G))
# # col=y
# pos0=graphviz_layout(G) #nx.spring_layout(G)
# pos=pos0 #nx.spring_layout(G)
##nx.draw_networkx(g,pos=pos, node_color=col,cmap=plot.cm.Reds)
# scale=1000
F,asoc=utilat.create_random_scalar_attributes(G,scale)
col=[F.node[i]['scalar_attribute'] for i in F.nodes()]
fig = plt.figure(figsize=(17,17))
starting_value_of_zero_node=F.node[0]['scalar_attribute']
sstt="Initial distribution of the opinions over graph nodes"#\n (diffusion source at node %s with initial attribute =%f)" %(F.nodes()[0],starting_value_of_zero_node)
plt.subplot(3,2,1).set_title(sstt)
# plt.set_cmap('cool')
nx.draw_networkx(G=G,pos=pos, node_color=col,vmin=0.,vmax=1.)
plt.axis('equal')
plt.axis('off')
# plt.figure(2)
sstt = "Time variation of opinions over graph nodes" #%F.nodes()[0]#,starting_value_of_zero_node)
plt.subplot(3,2,5).set_title(sstt)
# for i in F.nodes(data=True):
# print i
# print nx.numeric_assortativity_coefficient(F,'scalar_attribute_numeric')
iterat=[]
assort=[]
y=[F.node[i]['scalar_attribute'] for i in F.nodes()]
ckck=4
kckc=2
for ii in range(iterations):
# sa=0.05
checkin=True
# nd=F.nodes()[0]
# # print nd
# uu=0
# nei=nx.neighbors(F,nd)
# # print nei
# for nnei in nei:
# uu+=F.node[nnei]['scalar_attribute']
# if
# sau=(sa*uu/len(nei))+(1-sa)*F.node[nd]['scalar_attribute']
# insau=int(sau*scale)
# F.add_node(nd,scalar_attribute=sau,scalar_attribute_numeric=insau)
for nd in list(F.nodes()):
# sa=1-(1./nx.degree(F,nd))
uu=0
nei=list(nx.neighbors(F,nd))
# print nei
for nnei in nei:
uu+=F.node[nnei]['scalar_attribute']
X=F.node[nd]['scalar_attribute']
if X<0.5 and uu<0.5:
F.add_node(nd,scalar_attribute=min(X,uu),scalar_attribute_numeric=int(min(X,uu)*scale))
elif X>=0.5 and uu>=0.5:
F.add_node(nd,scalar_attribute=max(X,uu),scalar_attribute_numeric=int(max(X,uu)*scale))
# sau=(sb*uu/len(nei))+(1-sb)*F.node[nd]['scalar_attribute']
# insau=int(sau*scale)
# F.add_node(nd,scalar_attribute=sau,scalar_attribute_numeric=insau)
# for i in F.nodes(data=True):
# print i
# Checking for attributes equality
y1=y
y=[F.node[i]['scalar_attribute'] for i in F.nodes()]
if ii ==ckck and ii<10:
# col=['%.5f' %yy for yy in y]
col=[F.node[i]['scalar_attribute'] for i in F.nodes()]
sstt="Distribution of the opinions over graph nodes at %i iterations" %(ii+1)
plt.subplot(3,2,kckc).set_title(sstt)
nx.draw_networkx(G=G,pos=pos, node_color=col,vmin=0.,vmax=1.)
plt.axis('equal')
plt.axis('off')
ckck+=5
kckc+=1
plt.subplot(3,2,5)
# for yy in it.combinations(y,2):
# checkin=checkin and yy[0] -yy[1] <(1./scale)
# for yy in range(len(y1)):
# # print y[yy]-y1[yy]<(1./scale)
# checkin=checkin and y[yy]-y1[yy]<(0.1/(scale))
# # print checkin
# if checkin:
# break
asss=nx.numeric_assortativity_coefficient(F,'scalar_attribute_numeric')
if np.isnan(asss):
asss=1.
# print 'Iteration %i ==> %f' %(ii,asss),y
# print type(asss), asss<-1,asss>1
iterat.append(ii)
assort.append(asss)
# print nx.numeric_assortativity_coefficient(F,'scalar_attribute_numeric')
# y=[F.node[i]['scalar_attribute'] for i in F.nodes()]
plt.plot(F.nodes(),y)
# if asss==1. :
# break
plt.plot(F.nodes(),y,linewidth=3.)
sstt= "Time variation of the opinion assortativity coefficient"
plt.subplot(3,2,6).set_title(sstt)
# plt.figure(3)
plt.plot(iterat,assort)
plt.ylim(-1.1,1.1)
# plt.figure(3)
# plt.plot(F.nodes(),y)
# col=['%.5f' %yy for yy in y]
col=[F.node[i]['scalar_attribute'] for i in F.nodes()]
sstt="Distribution of the opinions over graph nodes at %i iterations"#\n (consensual attribute = %s)" %(iterations,col[0])
plt.subplot(3,2,4).set_title(sstt)
# plt.figure(4)
# print col
# print [F.node[i]['scalar_attribute_numeric'] for i in F.nodes()]
# pos=nx.spring_layout(G)
##nx.draw_networkx(g,pos=pos, node_color=col,cmap=plot.cm.Reds)
nx.draw_networkx(G=G,pos=pos, node_color=col,vmin=0.,vmax=1.)
plt.axis('equal')
plt.axis('off')
plt.show()
def sidif_sim(G,pos,b,iterations,scale=1000): #nodes,p,
# while True:
# # G=nx.connected_watts_strogatz_graph(25, 2, 0.8, tries=100)
# G=nx.erdos_renyi_graph(nodes,p)
# if nx.is_connected(G):
# break
# G.remove_nodes_from(nx.isolates(G))
# # col=y
# pos0=graphviz_layout(G) #nx.spring_layout(G)
# pos=pos0 #nx.spring_layout(G)
##nx.draw_networkx(g,pos=pos, node_color=col,cmap=plot.cm.Reds)
# scale=1000
F,asoc=utilat.create_random_scalar_attributes(G,scale)
col=[F.node[i]['scalar_attribute'] for i in F.nodes()]
fig = plt.figure(figsize=(17,17))
starting_value_of_zero_node=F.node[0]['scalar_attribute']
sstt="Initial distribution of the opinions over graph nodes\n (diffusion source at node %s with initial attribute = %f)" %(list(F.nodes())[0],starting_value_of_zero_node)
plt.subplot(3,2,1).set_title(sstt)
# plt.set_cmap('cool')
nx.draw_networkx(G=G,pos=pos, node_color=col,vmin=0.,vmax=1.)
plt.axis('equal')
plt.axis('off')
# plt.figure(2)
sstt = "Time variation of opinions over graph nodes" #%F.nodes()[0]#,starting_value_of_zero_node)
plt.subplot(3,2,5).set_title(sstt)
# for i in F.nodes(data=True):
# print i
# print nx.numeric_assortativity_coefficient(F,'scalar_attribute_numeric')
iterat=[]
assort=[]
y=[F.node[i]['scalar_attribute'] for i in F.nodes()]
ckck=4
kckc=2
for ii in range(iterations):
# sa=0.05
checkin=True
# nd=F.nodes()[0]
# # print nd
# uu=0
# nei=nx.neighbors(F,nd)
# # print nei
# for nnei in nei:
# uu+=F.node[nnei]['scalar_attribute']
# sau=F.node[nd]['scalar_attribute'] + b*(1. - F.node[nd]['scalar_attribute'])*(uu/len(nei))
# # sau=(sa*uu/len(nei))+(1-sa)*F.node[nd]['scalar_attribute']
# insau=int(sau*scale)
# F.add_node(nd,scalar_attribute=sau,scalar_attribute_numeric=insau)
for nd in list(F.nodes()):
# sa=1-(1./nx.degree(F,nd))
uu=0
nei=list(nx.neighbors(F,nd))
# print nei
for nnei in nei:
uu+=F.node[nnei]['scalar_attribute']
X=F.node[nd]['scalar_attribute']
Xnei=uu/len(nei)
# uX= len(nei)*(1-Xnei)
uX= b*(1. - X)*Xnei+X**2
# uX= b*(1. - Xnei)*X
# sau=(sb*uu/len(nei))+(1-sb)*F.node[nd]['scalar_attribute']
insau=int(uX*scale)
F.add_node(nd,scalar_attribute=uX,scalar_attribute_numeric=insau)
# for i in F.nodes(data=True):
# print i
# Checking for attributes equality
y1=y
y=[F.node[i]['scalar_attribute'] for i in F.nodes()]
if ii ==ckck and ii<10:
# col=['%.5f' %yy for yy in y]
col=[F.node[i]['scalar_attribute'] for i in F.nodes()]
sstt="Distribution of the opinions over graph nodes at %i iterations" %(ii+1)
plt.subplot(3,2,kckc).set_title(sstt)
nx.draw_networkx(G=G,pos=pos, node_color=col,vmin=0.,vmax=1.)
plt.axis('equal')
plt.axis('off')
ckck+=5
kckc+=1
plt.subplot(3,2,5)
# for yy in it.combinations(y,2):
# checkin=checkin and yy[0] -yy[1] <(1./scale)
# for yy in range(len(y1)):
# # print y[yy]-y1[yy]<(1./scale)
# checkin=checkin and y[yy]-y1[yy]<(0.1/(scale))
# # print checkin
# if checkin:
# break
asss=nx.numeric_assortativity_coefficient(F,'scalar_attribute_numeric')
if np.isnan(asss):
asss=1.
# print 'Iteration %i ==> %f' %(ii,asss),y
# print type(asss), asss<-1,asss>1
iterat.append(ii)
assort.append(asss)
# print nx.numeric_assortativity_coefficient(F,'scalar_attribute_numeric')
# y=[F.node[i]['scalar_attribute'] for i in F.nodes()]
plt.plot(F.nodes(),y)
# if asss==1. :
# break
plt.plot(F.nodes(),y,linewidth=3.)
sstt= "Time variation of the opinion assortativity coefficient"
plt.subplot(3,2,6).set_title(sstt)
# plt.figure(3)
plt.plot(iterat,assort)
plt.ylim(-1.1,1.1)
# plt.figure(3)
# plt.plot(F.nodes(),y)
# col=['%.5f' %yy for yy in y]
col=[F.node[i]['scalar_attribute'] for i in F.nodes()]
sstt="Distribution of the opinions over graph nodes at %i iterations\n (consensual attribute = %s)" %(iterations,col[0])
plt.subplot(3,2,4).set_title(sstt)
# plt.figure(4)
# print col
print [F.node[i]['scalar_attribute_numeric'] for i in F.nodes()]
# pos=nx.spring_layout(G)
##nx.draw_networkx(g,pos=pos, node_color=col,cmap=plot.cm.Reds)
nx.draw_networkx(G=G,pos=pos, node_color=col,vmin=0.,vmax=1.)
plt.axis('equal')
plt.axis('off')
plt.show()
def polinfluence_sim(G,pos,sa,iterations,scale=1000): #nodes,p,
# while True:
# # G=nx.connected_watts_strogatz_graph(25, 2, 0.8, tries=100)
# G=nx.erdos_renyi_graph(nodes,p)
# if nx.is_connected(G):
# break
# G.remove_nodes_from(nx.isolates(G))
# # col=y
# pos0=graphviz_layout(G) #nx.spring_layout(G)
# pos=pos0 #nx.spring_layout(G)
##nx.draw_networkx(g,pos=pos, node_color=col,cmap=plot.cm.Reds)
# scale=1000
F,asoc=utilat.create_random_scalar_attributes(G,scale)
col=[F.node[i]['scalar_attribute'] for i in F.nodes()]
fig = plt.figure(figsize=(17,17))
sstt="Initial distribution of the opinions over graph nodes" #\n (diffusion source at node %s with initial attribute =%f)" %(F.nodes()[0],starting_value_of_zero_node)
plt.subplot(3,2,1).set_title(sstt)
# plt.set_cmap('cool')
nx.draw_networkx(G=G,pos=pos, node_color=col,vmin=0.,vmax=1.)
plt.axis('equal')
plt.axis('off')
# plt.figure(2)
sstt = "Time variation of opinions over graph nodes" #%F.nodes()[0]#,starting_value_of_zero_node)
plt.subplot(3,2,5).set_title(sstt)
plt.ylim(-0.01,1.01)
# for i in F.nodes(data=True):
# print i
# print nx.numeric_assortativity_coefficient(F,'scalar_attribute_numeric')
iterat=[]
assort=[]
y=[F.node[i]['scalar_attribute'] for i in F.nodes()]
ckck=4
kckc=2
for ii in range(iterations):
# sa=0.05
checkin=True
# nd=F.nodes()[0]
# # # print nd
# uu=0
# nei=nx.neighbors(F,nd)
# # # print nei
# for nnei in nei:
# uu+=F.node[nnei]['scalar_attribute']
# if F.node[nd]['scalar_attribute'] < uu/len(nei):
# sau=sa*max(2.*F.node[nd]['scalar_attribute'] - uu/len(nei),0.)+(1-sa)*F.node[nd]['scalar_attribute']
# if F.node[nd]['scalar_attribute'] > uu/len(nei):
# sau=sa*min(2.*F.node[nd]['scalar_attribute'] - uu/len(nei),1.)+(1-sa)*F.node[nd]['scalar_attribute']
# # sau=(sa*uu/len(nei))+(1-sa)*F.node[nd]['scalar_attribute']
# insau=int(sau*scale)
# F.add_node(nd,scalar_attribute=sau,scalar_attribute_numeric=insau)
# nd=F.nodes()[-1]
# # # print nd
# uu=0
# nei=nx.neighbors(F,nd)
# # # print nei
# for nnei in nei:
# uu+=F.node[nnei]['scalar_attribute']
# if F.node[nd]['scalar_attribute'] < uu/len(nei):
# sau=sa*max(2.*F.node[nd]['scalar_attribute'] - uu/len(nei),0.)+(1-sa)*F.node[nd]['scalar_attribute']
# if F.node[nd]['scalar_attribute'] > uu/len(nei):
# sau=sa*min(2.*F.node[nd]['scalar_attribute'] - uu/len(nei),1.)+(1-sa)*F.node[nd]['scalar_attribute']
# # sau=(sa*uu/len(nei))+(1-sa)*F.node[nd]['scalar_attribute']
# insau=int(sau*scale)
# F.add_node(nd,scalar_attribute=sau,scalar_attribute_numeric=insau)
# if F.node[nd]['scalar_attribute'] < (uu/len(nei)):
# sau=(sa*max(2.*F.node[nd]['scalar_attribute'] - (uu/len(nei)),0.)+(1-sa)*F.node[nd]['scalar_attribute']
# if F.node[nd]['scalar_attribute'] > (uu/len(nei)):
# # else:
# sau=(sa*min(2.*F.node[nd]['scalar_attribute'] - (uu/len(nei)),1.)+(1-sa)*F.node[nd]['scalar_attribute']
# # sau=(sa*uu/len(nei))+(1-sa)*F.node[nd]['scalar_attribute']
# insau=int(sau*scale)
# F.add_node(nd,scalar_attribute=sau,scalar_attribute_numeric=insau)
# for nd in F.nodes()[1:-1]:
# # sa=1-(1./nx.degree(F,nd))
# uu=0
# nei=nx.neighbors(F,nd)
# # print nei
# for nnei in nei:
# uu+=F.node[nnei]['scalar_attribute']
# sau=(sa*uu/len(nei))+(1-sa)*F.node[nd]['scalar_attribute']
# insau=int(sau*scale)
# F.add_node(nd,scalar_attribute=sau,scalar_attribute_numeric=insau)
for nd in list(F.nodes()):
# sa=1-(1./nx.degree(F,nd))
uu=0
nei=list(nx.neighbors(F,nd))
# print nei
for nnei in nei:
uu+=F.node[nnei]['scalar_attribute']
X=F.node[nd]['scalar_attribute']
Xnei=uu/len(nei)
if X < Xnei:
uX=sa*max(2.*X - Xnei,0.)+(1-sa)*X
if X > Xnei:
uX=sa*min(2.*X - Xnei,1.)+(1-sa)*X
# uX=(sa*uu/len(nei))+(1-sa)*F.node[nd]['scalar_attribute']
insau=int(uX*scale)
F.add_node(nd,scalar_attribute=uX,scalar_attribute_numeric=insau)
# for i in F.nodes(data=True):
# print i
# Checking for attributes equality
y1=y
y=[F.node[i]['scalar_attribute'] for i in F.nodes()]
if ii ==ckck and ii<10:
# col=['%.5f' %yy for yy in y]
col=[F.node[i]['scalar_attribute'] for i in F.nodes()]
sstt="Distribution of the opinions over graph nodes at %i iterations" %(ii+1)
plt.subplot(3,2,kckc).set_title(sstt)
nx.draw_networkx(G=G,pos=pos, node_color=col,vmin=0.,vmax=1.)
plt.axis('equal')
plt.axis('off')
ckck+=5
kckc+=1
plt.subplot(3,2,5)
# for yy in it.combinations(y,2):
# checkin=checkin and yy[0] -yy[1] <(1./scale)
# for yy in range(len(y1)):
# # print y[yy]-y1[yy]<(1./scale)
# checkin=checkin and y[yy]-y1[yy]<(0.1/(scale))
# # print checkin
# if checkin:
# break
asss=nx.numeric_assortativity_coefficient(F,'scalar_attribute_numeric')
# if np.isnan(asss):
# asss=1.
# print 'Iteration %i ==> %f' %(ii,asss),y
# print type(asss), asss<-1,asss>1
iterat.append(ii)
assort.append(asss)
# print nx.numeric_assortativity_coefficient(F,'scalar_attribute_numeric')
# y=[F.node[i]['scalar_attribute'] for i in F.nodes()]
plt.plot(F.nodes(),y)
# if asss==1. :
# break
plt.plot(F.nodes(),y,linewidth=3.)
sstt= "Time variation of the opinion assortativity coefficient"
plt.subplot(3,2,6).set_title(sstt)
# plt.figure(3)
plt.plot(iterat,assort)
plt.ylim(-1.1,1.1)
# plt.figure(3)
# plt.plot(F.nodes(),y)
# col=['%.5f' %yy for yy in y]
col=[F.node[i]['scalar_attribute'] for i in F.nodes()]
sstt="Distribution of the opinions over graph nodes at %i iterations\n (polarized attributes = (%.2f, %.2f))" %(iterations,min(col),max(col))#col[0])
plt.subplot(3,2,4).set_title(sstt)
# plt.figure(4)
# print col
# print [F.node[i]['scalar_attribute_numeric'] for i in F.nodes()]
# pos=nx.spring_layout(G)
##nx.draw_networkx(g,pos=pos, node_color=col,cmap=plot.cm.Reds)
nx.draw_networkx(G=G,pos=pos, node_color=col,vmin=0.,vmax=1.)
plt.axis('equal')
plt.axis('off')
plt.show()
def cl_influence_sim(G,pos,sa1,sa2,iterations,scale=1000): # nodes,p, b1,b2,
# while True:
# # G=nx.connected_watts_strogatz_graph(25, 2, 0.8, tries=100)
# G=nx.erdos_renyi_graph(nodes,p)
# if nx.is_connected(G):
# break
# G.remove_nodes_from(nx.isolates(G))
# # col=y
# pos0=graphviz_layout(G) #nx.spring_layout(G)
# pos=pos0 #nx.spring_layout(G)
##nx.draw_networkx(g,pos=pos, node_color=col,cmap=plot.cm.Reds)
# scale=1000
F,asoc=utilat.create_random_scalar_attributes(G,scale)
col=[F.node[i]['scalar_attribute'] for i in F.nodes()]
fig = plt.figure(figsize=(17,17))
sstt="Initial distribution of the opinions over graph nodes" #\n (diffusion source at node %s with initial attribute =%f)" %(F.nodes()[0],starting_value_of_zero_node)
plt.subplot(3,2,1).set_title(sstt)
# plt.set_cmap('cool')
nx.draw_networkx(G=G,pos=pos, node_color=col,vmin=0.,vmax=1.)
plt.axis('equal')
plt.axis('off')
# plt.figure(2)
sstt = "Time variation of opinions over graph nodes" #%F.nodes()[0]#,starting_value_of_zero_node)
plt.subplot(3,2,5).set_title(sstt)
# for i in F.nodes(data=True):
# print i
# print nx.numeric_assortativity_coefficient(F,'scalar_attribute_numeric')
iterat=[]
assort=[]
y=[F.node[i]['scalar_attribute'] for i in F.nodes()]
ckck=4
kckc=2
for ii in range(iterations):
# sa=0.05
checkin=True
# nd=F.nodes()[0]
# # print nd
# uu=0
# nei=nx.neighbors(F,nd)
# # print nei
# for nnei in nei:
# uu+=F.node[nnei]['scalar_attribute']
# Xnei=uu/len(nei)
# X=F.node[nd]['scalar_attribute']
# if X <= 1/3. and Xnei <= 1/2.: #1/3.
# uX=(sa1*Xnei)+(1-sa1)*X
# elif 1/3. < X <= 2/3.: #and 1/3. < Xnei <= 2/3.:
# uX=(sa1*Xnei)+(1-sa1)*X
# elif X > 2/3. and Xnei > 1/2.: #2/3.
# uX=(sa1*Xnei)+(1-sa1)*X
# elif X <= 1/3. and 1/3. < Xnei <= 2/3.:
# uX=(sa2*Xnei)+(1-sa2)*X
# elif 1/3. < X <= 2/3. and Xnei <= 1/3.:
# uX=(sa2*Xnei)+(1-sa2)*X
# elif 1/3. < X <= 2/3. and 2/3. < Xnei:
# uX=(sa2*Xnei)+(1-sa2)*X
# elif 1/3. < Xnei <= 2/3. and 2/3. < X:
# uX=(sa2*Xnei)+(1-sa2)*X
# elif X <= 1/3. and 1/3. < Xnei <= 2/3.:
# uX=(sa2*Xnei)+(1-sa2)*X
# elif 1/3. < X <= 2/3. and Xnei <= 1/3.:
# uX=(sa2*Xnei)+(1-sa2)*X
# elif 1/3. < X <= 2/3. and 2/3. < Xnei:
# uX=(sa2*Xnei)+(1-sa2)*X
# elif 1/3. < Xnei <= 2/3. and 2/3. < X:
# uX=(sa2*Xnei)+(1-sa2)*X
# else:
# uX = X
# if b1 <= abs(X - Xnei) <= b2:
# uX=(sa*Xnei)+(1-sa)*X
# # else:
# # uX = X
# # elif X < Xnei:
# # uX=0
# # elif X > Xnei:
# # uX=1
# elif X < Xnei:
# uX=sa*max(2.*X - Xnei,0.)+(1-sa)*X
# elif X > Xnei:
# uX=sa*min(2.*X - Xnei,1.)+(1-sa)*X
# insau=int(uX*scale)
# F.add_node(nd,scalar_attribute=uX,scalar_attribute_numeric=insau)
for nd in list(F.nodes()):
# sa=1-(1./nx.degree(F,nd))
uu=0
nei=list(nx.neighbors(F,nd))
# print nei
for nnei in nei:
uu+=F.node[nnei]['scalar_attribute']
X=F.node[nd]['scalar_attribute']
Xnei=uu/len(nei)
if X <= 1/3. and Xnei <= 1/2.: #1/3.
uX=(sa1*Xnei)+(1-sa1)*X
elif 1/3. < X <= 2/3.: #and 1/3. < Xnei <= 2/3.:
uX=(sa1*Xnei)+(1-sa1)*X
elif X > 2/3. and Xnei > 1/2.: #2/3.
uX=(sa1*Xnei)+(1-sa1)*X
# elif X <= 1/3. and 1/3. < Xnei <= 2/3.:
# uX=(sa2*Xnei)+(1-sa2)*X
# elif 1/3. < X <= 2/3. and Xnei <= 1/3.:
# uX=(sa2*Xnei)+(1-sa2)*X
# elif 1/3. < X <= 2/3. and 2/3. < Xnei:
# uX=(sa2*Xnei)+(1-sa2)*X
# elif 1/3. < Xnei <= 2/3. and 2/3. < X:
# uX=(sa2*Xnei)+(1-sa2)*X
else:
uX = X
# print uX
# if b1 <= abs(X - Xnei) <= b2:
# uX=(sa*Xnei)+(1-sa)*X
# # else:
# # uX = X
# # elif X < Xnei:
# # uX=0
# # elif X > Xnei:
# # uX=1
# elif X < Xnei:
# uX=sa*max(2.*X - Xnei,0.)+(1-sa)*X
# elif X > Xnei:
# uX=sa*min(2.*X - Xnei,1.)+(1-sa)*X
# # uX=(sa*Xnei)+(1-sa)*X
insau=int(uX*scale)
F.add_node(nd,scalar_attribute=uX,scalar_attribute_numeric=insau)