G=nx.DiGraph()

upp=nx.read_graphml('upperlevel_hashtags.graphml')

for ed in upp.edges(data=True):

G.add_edge(ed[0],ed[1],attr_dict=ed[2])

G.add_edge(ed[1],ed[0],attr_dict=ed[2])

mid=nx.read_graphml('friendship_graph.graphml')

for ed in mid.edges(data=True):

G.add_edge(ed[0],ed[1],attr_dict=ed[2])

inter=nx.read_graphml('interlevel_hashtags.graphml')

for ed in inter.edges(data=True):

G.add_edge(ed[0],ed[1],attr_dict=ed[2])

G.add_edge(ed[1],ed[0],attr_dict=ed[2])

down=nx.read_graphml('retweet.graphml')

mapping_f={}

for i,v in enumerate(down.nodes()):

mapping_f[v]='%iretweet_net' %i

for ed in down.edges(data=True):

G.add_edge(mapping_f[ed[0]],mapping_f[ed[1]],attr_dict=ed[2])

for nd in mid.nodes():

if nd in mapping_f:

G.add_edge(nd,mapping_f[nd])

G.add_edge(mapping_f[nd],nd)

nx.write_graphml(G,'joined_3layerdigraph.graphm')

G=nx.DiGraph()

upp=nx.read_graphml('upperlevel_hashtags.graphml')

for ed in upp.edges(data=True):

G.add_edge(ed[0],ed[1],attr_dict=ed[2])

G.add_edge(ed[1],ed[0],attr_dict=ed[2])

mid=nx.read_graphml('friendship_graph.graphml')

for ed in mid.edges(data=True):

G.add_edge(ed[0],ed[1],attr_dict=ed[2])

inter=nx.read_graphml('interlevel_hashtags.graphml')

for ed in inter.edges(data=True):

G.add_edge(ed[0],ed[1],attr_dict=ed[2])

G.add_edge(ed[1],ed[0],attr_dict=ed[2])

nx.write_graphml(G,'joined_hasht_friends.graphm')

G=nx.DiGraph()

upp=nx.read_graphml('upperlevel_hashtags.graphml')

for ed in upp.edges(data=True):

G.add_edge(ed[0],ed[1],attr_dict=ed[2])

G.add_edge(ed[1],ed[0],attr_dict=ed[2])

down=nx.read_graphml('retweet.graphml')

for ed in down.edges(data=True):

G.add_edge(ed[0],ed[1],attr_dict=ed[2])

inter=nx.read_graphml('interlevel_hashtags.graphml')

for ed in inter.edges(data=True):

G.add_edge(ed[0],ed[1],attr_dict=ed[2])

G.add_edge(ed[1],ed[0],attr_dict=ed[2])

nx.write_graphml(G,'joined_hasht_retweet.graphm')

# print layer1

npartition = list(nx.weakly_connected_components(G))

print len(npartition)

# G=nx.Graph(G)

layers={'layer1':layer1,'layer2':layer2,'layer3':layer3}

broken_partition={}

for i,v in enumerate(npartition):

vs=set(v)

for ii,vv in layers.items():

papa=vs.intersection(set(vv))

if len(papa)==len(v):

broken_partition['a_%i_%s_s' %(i,ii)]=v

elif len(papa)>0:

broken_partition['b_%i_%s' %(i,ii)]=list(papa)

vs=vs-set(vv)

# print rbroken_partition

broken_graph=nx.Graph()

rbroken_partition=dict()

colors=[name for name,hex in matplotlib.colors.cnames.iteritems()]

colors=list(set(colors)-set(['red','blue','green']))

cl=dict()

for i,v in broken_partition.items():

name=i.split('_')

for ii in v:

# print ii

rbroken_partition[ii]=i

if name[-1]=='s':

cl[name[1]]=colors.pop()

elif name[0]=='b' and not cl.has_key(name[1]):

cl[name[1]]=colors.pop()

# print rbroken_partition

for i,v in rbroken_partition.items():

name=v.split('_')

broken_graph.add_node(v,color=cl[name[1]])

edg=G[i]

# print edg

for j in edg:

if j not in broken_partition[v]:

# print j

if not broken_graph.has_edge(v,rbroken_partition[j]):

broken_graph.add_edge(v,rbroken_partition[j])

import community as cm

G=nx.Graph(G)

partition = cm.best_partition(G)

print len(set(partition.values()))

layers={'layer1':layer1,'layer2':layer2,'layer3':layer3}

broken_partition={}

npartition={}

for i,v in partition.items():

if v not in npartition:

npartition[v]=[i]

else:

npartition[v].append(i)

for i,v in npartition.items():

vs=set(v)

for ii,vv in layers.items():

papa=vs.intersection(set(vv))

if len(papa)==len(v):

broken_partition['a_%i_%s_s' %(i,ii)]=v

elif len(papa)>0:

broken_partition['b_%i_%s' %(i,ii)]=list(papa)

vs=vs-set(vv)

broken_graph=nx.Graph()

rbroken_partition=dict()

colors=[name for name,hex in matplotlib.colors.cnames.iteritems()]

colors=list(set(colors)-set(['red','blue','green']))

cl=dict()

for i,v in broken_partition.items():

name=i.split('_')

for ii in v:

rbroken_partition[ii]=i

if name[-1]=='s':

cl[name[1]]=colors.pop()

elif name[0]=='b' and not cl.has_key(name[1]):

cl[name[1]]=colors.pop()

for i,v in rbroken_partition.items():

name=v.split('_')

broken_graph.add_node(v,color=cl[name[1]])

edg=G[i]

# print edg

for j in edg:

# print j

if j not in broken_partition[v]:

# print j

if not broken_graph.has_edge(v,rbroken_partition[j]):

broken_graph.add_edge(v,rbroken_partition[j])

# npartition = list(nx.connected_components(G))

for i in G.nodes():

# attr=G.node[i]

# print attr

G.add_node(i,attr_dict=G.node[i],best_partition=partition[i])

if i in layer1:

G.add_node(i,attr_dict=G.node[i],layers_3='1')

elif i in layer2:

G.add_node(i,attr_dict=G.node[i],layers_3='2')

else:

G.add_node(i,attr_dict=G.node[i],layers_3='3')

# rr=nx.attribute_assortativity_coefficient(G,'layers_3')

# s_title='Assortativity_coef(3_layers)= %.2f' %rr

# plt.title(s_title,{'size': '20'})

# import matplotlib

import matplotlib.pyplot as plt

from matplotlib.patches import Ellipse, Polygon

from collections import Counter

import random

if layout:

pos=nx.spring_layout(G)

else:

pos=nx.random_layout(G)

top_set=set()

bottom_set=set()

middle_set=set()

down=[]

right=[]

left=[]

mlayer_part={}

for i in broken_partition:

ii=i.split('_')

if ii[1] not in mlayer_part:

mlayer_part[ii[1]]=set([ii[2]])

else:

mlayer_part[ii[1]].add(ii[2])

layers_m=Counter()

for k,v in mlayer_part.items():

if len(v)==1:

layers_m[1]+=1

elif len(v)==2:

layers_m[2]+=1

elif len(v)==3:

layers_m[3]+=1

else:

print k,v

broken_pos={}

mlayer_part={}

for i in broken_partition:

# print i.split('_')

ii=i.split('_')

if ii[1] not in mlayer_part:

mlayer_part[ii[1]]=set([ii[2]])

else:

mlayer_part[ii[1]].add(ii[2])

layers_m=Counter()

for k,v in mlayer_part.items():

if len(v)==1:

layers_m[1]+=1

elif len(v)==2:

layers_m[2]+=1

elif len(v)==3:

layers_m[3]+=1

else:

print k,v

singles=0

for i,v in broken_partition.items():

name=i.split('_')

if name[-1]=='s':

singles+=1

ndnd=random.choice(v)

npos=pos[ndnd]

if ndnd in layer1:

broken_pos[i]=[d2*(npos[0]),d2*(npos[1]+d1)]

top_set.add(i)

left.append(broken_pos[i])

elif ndnd in layer2:

broken_pos[i]=[d2*(npos[0]),d2*(npos[1]-d1)]

bottom_set.add(i)

right.append(broken_pos[i])

else:

broken_pos[i]=[d2*npos[0],d2*(npos[1])]

middle_set.add(i)

down.append(broken_pos[i])

xleft=[i[0] for i in left]

yleft=[i[1] for i in left]

aleft = [min(xleft)-d1/2.,max(yleft)+d1/2.-d3]

bleft = [max(xleft)+d1/2.,max(yleft)+d1/2.+d3]

cleft = [max(xleft)+d1/2.-d4,min(yleft)-d1/2.+d3]

dleft = [min(xleft)-d1/2.-d4,min(yleft)-d1/2.-d3]

xright=[i[0] for i in right]

yright=[i[1] for i in right]

aright = [min(xright)-d1/2.,max(yright)+d1/2.-d3]

bright = [max(xright)+d1/2.,max(yright)+d1/2.+d3]

cright = [max(xright)+d1/2.-d4,min(yright)-d1/2.+d3]

dright = [min(xright)-d1/2.-d4,min(yright)-d1/2.-d3]

xdown=[i[0] for i in down]

ydown=[i[1] for i in down]

adown = [min(xdown)-d1/2.,max(ydown)+d1/2.-d3]

bdown = [max(xdown)+d1/2.,max(ydown)+d1/2.+d3]

cdown = [max(xdown)+d1/2.-d4,min(ydown)-d1/2.+d3]

ddown = [min(xdown)-d1/2.-d4,min(ydown)-d1/2.-d3]

nodesSizes=[]

fig=plt.figure(figsize=(20,20))

ax=fig.add_subplot(111)

ax.add_patch(Polygon([aleft,bleft,cleft,dleft],color='r',alpha=0.1))

plt.plot([aleft[0],bleft[0],cleft[0],dleft[0],aleft[0]],[aleft[1],bleft[1],cleft[1],dleft[1],aleft[1]],'-r')

ax.add_patch(Polygon([aright,bright,cright,dright],color='b',alpha=0.1))

plt.plot([aright[0],bright[0],cright[0],dright[0],aright[0]],[aright[1],bright[1],cright[1],dright[1],aright[1]],'-b')

ax.add_patch(Polygon([adown,bdown,cdown,ddown],color='g',alpha=0.1))

plt.plot([adown[0],bdown[0],cdown[0],ddown[0],adown[0]],[adown[1],bdown[1],cdown[1],ddown[1],adown[1]],'-g')

nodeSize=[nodesize*len(broken_partition[i]) for i in list(top_set)]

nodeColor=[broken_graph.node[i]['color'] for i in list(top_set) ]

nodesSizes=[i for i in nodeSize]

nx.draw_networkx_nodes(broken_graph,broken_pos, nodelist=list(top_set),node_shape='s',node_color=nodeColor,alpha=1,node_size=nodeSize)

nodeSize=[nodesize*len(broken_partition[i]) for i in list(middle_set)]

nodeColor=[broken_graph.node[i]['color'] for i in list(middle_set) ]

for nds in nodeSize:

nodesSizes.append(nds)

nx.draw_networkx_nodes(broken_graph,broken_pos, nodelist=list(middle_set),node_shape='s',node_color=nodeColor,alpha=1,node_size=nodeSize)

nodeSize=[nodesize*len(broken_partition[i]) for i in list(bottom_set)]

nodeColor=[broken_graph.node[i]['color'] for i in list(bottom_set) ]

for nds in nodeSize:

nodesSizes.append(nds)

nx.draw_networkx_nodes(broken_graph,broken_pos,nodelist=list(bottom_set),node_shape='s',node_color=nodeColor,alpha=1,node_size=nodeSize)

if withlabels:

nx.draw_networkx_labels(G,pos)

lay1_edges=[ed for ed in G.edges() if ed[0] in layer1 and ed[1] in layer1]

lay2_edges=[ed for ed in G.edges() if ed[0] in layer2 and ed[1] in layer2]

lay3_edges=[ed for ed in G.edges() if ed[0] in layer3 and ed[1] in layer3]

nx.draw_networkx_edges(broken_graph,broken_pos,alpha=0.3)

for i,v in broken_partition.items():

for nd in v:

atrr=G.node[nd]

G.add_node(nd,attr_dict=atrr,asso=i)

# print G.nodes(data=True)

# rr=nx.attribute_assortativity_coefficient(G,'asso')

# print 'Attribute assortativity coefficient wrt layer partition (old)= %f' %orr

nodesSizes=sorted(nodesSizes,reverse=True)

title_s='%i weakly connected components (%i 3-layered, %i 2-layered, %i 1-layered)\n Sizes of 6 biggest weakly connected components:(%i,%i,%i,%i,%i,%i)' %(len(npartition),layers_m[3],layers_m[2],layers_m[1],nodesSizes[0],nodesSizes[1],nodesSizes[2],nodesSizes[3],nodesSizes[4],nodesSizes[5])

# title_s='%i connected components (%i 3-layered, %i 2-layered, %i 1-layered)' %(len(npartition),layers_m[3],layers_m[2],layers_m[1]) # %(len(npartition),len(npartition)-singles,singles)

plt.title(title_s,{'size': '20'})

plt.axis('off')

# import matplotlib

import matplotlib.pyplot as plt

from matplotlib.patches import Ellipse, Polygon

from collections import Counter

import random

if layout:

pos=nx.spring_layout(G)

else:

pos=nx.random_layout(G)

top_set=set()

bottom_set=set()

middle_set=set()

down=[]

right=[]

left=[]

mlayer_part={}

for i in broken_partition:

ii=i.split('_')

if ii[1] not in mlayer_part:

mlayer_part[ii[1]]=set([ii[2]])

else:

mlayer_part[ii[1]].add(ii[2])

layers_m=Counter()

for k,v in mlayer_part.items():

if len(v)==1:

layers_m[1]+=1

elif len(v)==2:

layers_m[2]+=1

elif len(v)==3:

layers_m[3]+=1

else:

print k,v

broken_pos={}

mlayer_part={}

for i in broken_partition:

# print i.split('_')

ii=i.split('_')

if ii[1] not in mlayer_part:

mlayer_part[ii[1]]=set([ii[2]])

else:

mlayer_part[ii[1]].add(ii[2])

layers_m=Counter()

for k,v in mlayer_part.items():

if len(v)==1:

layers_m[1]+=1

elif len(v)==2:

layers_m[2]+=1

elif len(v)==3:

layers_m[3]+=1

else:

print k,v

singles=0

for i,v in broken_partition.items():

name=i.split('_')

if name[-1]=='s':

singles+=1

ndnd=random.choice(v)

npos=pos[ndnd]

if ndnd in layer1:

broken_pos[i]=[d2*(npos[0]),d2*(npos[1]+d1)]

top_set.add(i)

left.append(broken_pos[i])

elif ndnd in layer2:

broken_pos[i]=[d2*(npos[0]),d2*(npos[1]-d1)]

bottom_set.add(i)

right.append(broken_pos[i])

else:

broken_pos[i]=[d2*npos[0],d2*(npos[1])]

middle_set.add(i)

down.append(broken_pos[i])

xleft=[i[0] for i in left]

yleft=[i[1] for i in left]

aleft = [min(xleft)-d1/2.,max(yleft)+d1/2.-d3]

bleft = [max(xleft)+d1/2.,max(yleft)+d1/2.+d3]

cleft = [max(xleft)+d1/2.-d4,min(yleft)-d1/2.+d3]

dleft = [min(xleft)-d1/2.-d4,min(yleft)-d1/2.-d3]

xright=[i[0] for i in right]

yright=[i[1] for i in right]

aright = [min(xright)-d1/2.,max(yright)+d1/2.-d3]

bright = [max(xright)+d1/2.,max(yright)+d1/2.+d3]

cright = [max(xright)+d1/2.-d4,min(yright)-d1/2.+d3]

dright = [min(xright)-d1/2.-d4,min(yright)-d1/2.-d3]

xdown=[i[0] for i in down]

ydown=[i[1] for i in down]

adown = [min(xdown)-d1/2.,max(ydown)+d1/2.-d3]

bdown = [max(xdown)+d1/2.,max(ydown)+d1/2.+d3]

cdown = [max(xdown)+d1/2.-d4,min(ydown)-d1/2.+d3]

ddown = [min(xdown)-d1/2.-d4,min(ydown)-d1/2.-d3]

nodesSizes=[]

fig=plt.figure(figsize=(20,20))

ax=fig.add_subplot(111)

ax.add_patch(Polygon([aleft,bleft,cleft,dleft],color='r',alpha=0.1))

plt.plot([aleft[0],bleft[0],cleft[0],dleft[0],aleft[0]],[aleft[1],bleft[1],cleft[1],dleft[1],aleft[1]],'-r')

ax.add_patch(Polygon([aright,bright,cright,dright],color='b',alpha=0.1))

plt.plot([aright[0],bright[0],cright[0],dright[0],aright[0]],[aright[1],bright[1],cright[1],dright[1],aright[1]],'-b')

ax.add_patch(Polygon([adown,bdown,cdown,ddown],color='g',alpha=0.1))

plt.plot([adown[0],bdown[0],cdown[0],ddown[0],adown[0]],[adown[1],bdown[1],cdown[1],ddown[1],adown[1]],'-g')

nodeSize=[nodesize*len(broken_partition[i]) for i in list(top_set)]

nodeColor=[broken_graph.node[i]['color'] for i in list(top_set) ]

nodesSizes=[i for i in nodeSize]

nx.draw_networkx_nodes(broken_graph,broken_pos, nodelist=list(top_set),node_shape='s',node_color=nodeColor,alpha=1,node_size=nodeSize)

nodeSize=[nodesize*len(broken_partition[i]) for i in list(middle_set)]

nodeColor=[broken_graph.node[i]['color'] for i in list(middle_set) ]

for nds in nodeSize:

nodesSizes.append(nds)

nx.draw_networkx_nodes(broken_graph,broken_pos, nodelist=list(middle_set),node_shape='s',node_color=nodeColor,alpha=1,node_size=nodeSize)

nodeSize=[nodesize*len(broken_partition[i]) for i in list(bottom_set)]

nodeColor=[broken_graph.node[i]['color'] for i in list(bottom_set) ]

for nds in nodeSize:

nodesSizes.append(nds)

nx.draw_networkx_nodes(broken_graph,broken_pos,nodelist=list(bottom_set),node_shape='s',node_color=nodeColor,alpha=1,node_size=nodeSize)

if withlabels:

nx.draw_networkx_labels(G,pos)

lay1_edges=[ed for ed in G.edges() if ed[0] in layer1 and ed[1] in layer1]

lay2_edges=[ed for ed in G.edges() if ed[0] in layer2 and ed[1] in layer2]

lay3_edges=[ed for ed in G.edges() if ed[0] in layer3 and ed[1] in layer3]

nx.draw_networkx_edges(broken_graph,broken_pos,alpha=0.3)

for i,v in broken_partition.items():

for nd in v:

atrr=G.node[nd]

G.add_node(nd,attr_dict=atrr,asso=i)

# print G.nodes(data=True)

# rr=nx.attribute_assortativity_coefficient(G,'asso')

# print 'Attribute assortativity coefficient wrt layer partition (old)= %f' %orr

nodesSizes=sorted(nodesSizes,reverse=True)

title_s='%i Communities (%i 3-layered, %i 2-layered, %i 1-layered)\n Sizes of 6 biggest Communities:(%i,%i,%i,%i,%i,%i)' %(len(npartition),layers_m[3],layers_m[2],layers_m[1],nodesSizes[0],nodesSizes[1],nodesSizes[2],nodesSizes[3],nodesSizes[4],nodesSizes[5])

# title_s='%i connected components (%i 3-layered, %i 2-layered, %i 1-layered)' %(len(npartition),layers_m[3],layers_m[2],layers_m[1]) # %(len(npartition),len(npartition)-singles,singles)

plt.title(title_s,{'size': '20'})

plt.axis('off')