def main(argv):
#Standardvalues
partitionfile = "data/partitions/final_partitions_p100_200_0.2.csv"
project = "584"
to_pajek = False
try:
opts, args = getopt.getopt(argv,"p:s:o")
except getopt.GetoptError:
print 'individual_bridging.py -p <project_name> -s <partitionfile> -o [if you want pajek output]'
sys.exit(2)
for opt, arg in opts:
if opt in ("-p"):
project = arg
elif opt in ("-s"):
partitionfile = arg
elif opt in ("-o"):
to_pajek = True
else:
print 'individual_bridging.py -p <project_name> -s <partitionfile> -o [if you want pajek output]'
print "##################### INDIVIDUAL BRIDGING ########################"
print "Project %s " % project
print "Partition %s" % partitionfile
csv_bridging_writer = csv.writer(open('results/spss/individual bridging/%s_individual_bridging.csv' % project, 'wb'))
csv_bridging_writer.writerow(["Name", "Group1", "Group2", "Number_between_ties",
"Competing_lists",
"FF_bin_degree", "FF_bin_in_degree", "FF_bin_out_degree",
"FF_bin_betweeness",
#"FF_c_size","FF_c_density","FF_c_hierarchy","FF_c_index",
"FF_own_group_in_volume", "FF_other_group_in_volume",
"FF_own_group_out_volume", "FF_other_group_out_volume",
"AT_bin_degree", "AT_bin_in_degree", "AT_bin_out_degree",
"AT_bin_betweeness",
"AT_volume_in", "AT_volume_out",
#"AT_c_size","AT_c_density","AT_c_hierarchy","AT_c_index",
"AT_own_group_in_volume", "AT_other_group_in_volume",
"AT_own_group_out_volume", "AT_other_group_out_volume",
"RT_total_volume_in", "RT_total_volume_out",
"RT_own_group_in_volume", "RT_other_group_in_volume",
"RT_own_group_out_volume", "RT_other_group_out_volume"])
#Read in the list-listings for individuals
listings = {}
indiv_reader = csv.reader(open(partitionfile))
for row in indiv_reader:
listings[row[0]] = {"group":row[1],"place":int(row[2]), "competing_lists": int(row[3])}
#Read in the edges between the groups and sort them
GROUPS = 80 # 80x200 ~ 16000 individuals for analysis
reader = csv.reader(open("results/%s_bridging_edges.csv" % project, "rb"), delimiter=",")
edges = []
for row in reader:
edges.append({"group1":row[0],"group2":row[1], "count":float(row[2])})
edges_sorted = sorted(edges, key=lambda k: k["count"])
distance_between_samples = int(float(len(edges_sorted)) / GROUPS)
if distance_between_samples == 0: distance_between_samples = 1 #Minimal Distance
iterator = 0
# Read in the partition
tmp = hp.get_partition(partitionfile)
partitions = tmp[0]
groups = tmp[1]
# Read in the networks
FF_all = nx.read_edgelist('data/networks/%s_FF.edgelist' % project, nodetype=str, data=(('weight',float),),create_using=nx.DiGraph())
AT_all = nx.read_edgelist('data/networks/%s_solr_AT.edgelist' % project, nodetype=str, data=(('weight',float),),create_using=nx.DiGraph())
RT_all = nx.read_edgelist('data/networks/%s_solr_RT.edgelist' % project, nodetype=str, data=(('weight',float),),create_using=nx.DiGraph())
i = 0
for partition in partitions:
for node in partition:
FF_all.add_node(node, group = groups[i]) # Add nodes
AT_all.add_node(node, group = groups[i])
RT_all.add_node(node, group = groups[i])
i += 1
while iterator < len(edges_sorted):
#Genereate a subgraph consisting out of two partitions
# Problem: With n= 2(pairs of 2) and k = 200 (~number of groups) we can generate 200 ^ 200 /2 combinations. How to generate the two pairs?
# Solution 1: By Random
# Solution 2: Based on the ordered tie strength between groups from the group bridging step
# e.g. [10,9,8,7,6,5,0] take every xth element to create set with this size [10,8,6,0]
# TODO Bin same edges with same weight into the same category and then select a grop by random
selected_edge = edges_sorted[iterator]
group1 = selected_edge["group1"]
group2 = selected_edge["group2"]
index1 = groups.index(group1)
index2 = groups.index(group2)
print "%s : %s with %s of strength %s" % (iterator, group1, group2, selected_edge["count"])
# Create Subgraphs
S_FF = FF_all.subgraph(partitions[index1]+partitions[index2])
S_FF.name = "%s_%s" % (group1, group2)
S_AT = AT_all.subgraph(partitions[index1]+partitions[index2])
S_AT.name = "%s_%s" % (group1, group2)
S_RT = RT_all.subgraph(partitions[index1]+partitions[index2])
S_RT.name = "%s_%s" % (group1, group2)
iterator += distance_between_samples # Make equidistant steps in with the iterator
#Optional Output to pajek
if to_pajek:
print "Generating pajek output for %s %s" % (groups[index1], groups[index2])
#Relabel for pajek
def mapping(x):
return "\"%s\"" % x
H_FF = nx.relabel_nodes(S_FF,mapping)
H_AT = nx.relabel_nodes(S_AT,mapping)
H_RT = nx.relabel_nodes(S_RT,mapping)
#Write it to disk
nx.write_pajek(H_FF,"results/networks/pairs/%s_%s_%s_pair_FF.net" % (project, groups[index1], groups[index2]))
nx.write_pajek(H_AT,"results/networks/pairs/%s_%s_%s_pair_AT.net" % (project, groups[index1], groups[index2]))
nx.write_pajek(H_RT,"results/networks/pairs/%s_%s_%s_pair_RT.net" % (project, groups[index1], groups[index2]))
################ MEASURES ################
## FF measures
dFF_bin = nx.degree_centrality(S_FF)
dFF_bin_in = nx.in_degree_centrality(S_FF)
dFF_bin_out = nx.out_degree_centrality(S_FF)
dFF_bin_betweeness = nx.betweenness_centrality(S_FF)
# Structural Holes has problems, probably with nonconnected networks (eventually compte bigest component first)
# dFF_struc = sx.structural_holes(S_FF)
# Which one is own group which one is other ?
dFF_group1_vol_in = hp.individual_in_volume(S_FF,group1)
dFF_group2_vol_in = hp.individual_in_volume(S_FF,group2)
dFF_group1_vol_out = hp.individual_out_volume(S_FF,group1)
dFF_group2_vol_out = hp.individual_out_volume(S_FF,group2)
## AT Measures
dAT_bin = nx.degree_centrality(S_AT)
dAT_bin_in = nx.in_degree_centrality(S_AT)
dAT_bin_out = nx.out_degree_centrality(S_AT)
dAT_bin_betweeness = nx.betweenness_centrality(S_AT)
# Why can here the structural holes not be computed?
#dAT_struc = sx.structural_holes(S_AT)
dAT_group1_vol_in = hp.individual_in_volume(S_AT,group1)
dAT_group2_vol_in = hp.individual_in_volume(S_AT,group2)
dAT_group1_vol_out = hp.individual_out_volume(S_AT,group1)
dAT_group2_vol_out = hp.individual_out_volume(S_AT,group2)
############### DEPENDENT VARIABLES ###########
dRT_group1_vol_in = hp.individual_in_volume(S_RT,group1)
dRT_group2_vol_in = hp.individual_in_volume(S_RT,group2)
dRT_group1_vol_out = hp.individual_out_volume(S_RT,group1)
dRT_group2_vol_out = hp.individual_out_volume(S_RT,group2)
############ OUTPUT ###########################
#Arrange it in a list and output
for node in dFF_bin.keys():
# Depending if the node is in partition 1 or two the definition of "own" and "other" changes.
if node in partitions[index1]:
#FF
FF_own_group_in_volume = dFF_group1_vol_in[node]
FF_own_group_out_volume = dFF_group1_vol_out[node]
FF_other_group_in_volume = dFF_group2_vol_in[node]
FF_other_group_out_volume = dFF_group2_vol_out[node]
#AT
AT_own_group_in_volume = dAT_group1_vol_in[node]
AT_own_group_out_volume = dAT_group1_vol_out[node]
AT_other_group_in_volume = dAT_group2_vol_in[node]
AT_other_group_out_volume = dAT_group2_vol_out[node]
#RT
RT_own_group_in_volume = dRT_group1_vol_in[node]
RT_own_group_out_volume = dRT_group1_vol_out[node]
RT_other_group_in_volume = dRT_group2_vol_in[node]
RT_other_group_out_volume = dRT_group2_vol_out[node]
else:
FF_own_group_in_volume = dFF_group2_vol_in[node]
FF_own_group_out_volume = dFF_group2_vol_out[node]
FF_other_group_in_volume = dFF_group1_vol_in[node]
FF_other_group_out_volume = dFF_group1_vol_out[node]
#AT
AT_own_group_in_volume = dAT_group2_vol_in[node]
AT_own_group_out_volume = dAT_group2_vol_out[node]
AT_other_group_in_volume = dAT_group1_vol_in[node]
AT_other_group_out_volume = dAT_group1_vol_out[node]
#RT
RT_own_group_in_volume = dRT_group2_vol_in[node]
RT_own_group_out_volume = dRT_group2_vol_out[node]
RT_other_group_in_volume = dRT_group1_vol_in[node]
RT_other_group_out_volume = dRT_group1_vol_out[node]
csv_bridging_writer.writerow([node, group1, group2,selected_edge["count"],
listings[node]["competing_lists"],
dFF_bin[node], dFF_bin_in[node], dFF_bin_out[node],
dFF_bin_betweeness[node],
#dFF_struc[node]['C-Size'],dFF_struc[node]['C-Density'],dFF_struc[node]['C-Hierarchy'],dFF_struc[node]['C-Index'],
FF_own_group_in_volume, FF_other_group_in_volume,
FF_own_group_out_volume, FF_other_group_out_volume,
dAT_bin[node], dAT_bin_in[node], dAT_bin_out[node],
dAT_bin_betweeness[node],
S_AT.in_degree(node,weight="weight"), S_AT.out_degree(node,weight="weight"),
#dAT_struc[node]['C-Size'],dAT_struc[node]['C-Density'],dAT_struc[node]['C-Hierarchy'],dAT_struc[node]['C-Index'],
AT_own_group_in_volume, AT_other_group_in_volume,
AT_own_group_out_volume, AT_other_group_out_volume,
S_RT.in_degree(node,weight="weight"), S_RT.out_degree(node,weight="weight"),
RT_own_group_in_volume, RT_other_group_in_volume,
RT_own_group_out_volume, RT_other_group_out_volume,
])
if nx.average_shortest_path_length(FF) == nx.average_shortest_path_length(FF_bin): print "NOTICE: Group Average path length is is BINARY"
if hp.average_tie_strength(FF) == hp.average_tie_strength(FF_bin): print "NOTICE: Group Average tie strength path is is BINARY"
################# Individual Bonding measures ################
#dFF_degree = FF.degree("a1")
dFF = nx.degree_centrality(FF) # Binarized undirected
dFF_in = nx.in_degree_centrality(FF) #People that follow me in the network binarized
dFF_out = nx.out_degree_centrality(FF) #People that I follow in the network binarized
dFF_closeness = nx.closeness_centrality(FF) # Non-directed and binarized
#dFF_pagerank = nx.pagerank(FF)
dFF_eigenvector = nx.eigenvector_centrality(FF.to_undirected()) # Undirected and binarized
dFF_rec = hp.individual_reciprocity(FF) # Individual Reciprocity
dFF_avg_tie = hp.individual_average_tie_strength(FF) # Individual average tie strength
dFF_in_volume = hp.individual_in_volume(FF) #compute the volume of all incoming ties
dFF_out_volume = hp.individual_out_volume(FF) #compute the volume of all outgoing ties
# Test the output of NetworkX against UCINET
if names[i] == "a":
print "######################## INDIVIDUAL MEASURES TEST of BINARY #####################"
a2 = "a2"
if nx.degree_centrality(FF) == nx.degree_centrality(FF_bin): print "NOTICE: Degree centrality is BINARY"
if nx.in_degree_centrality(FF) == nx.in_degree_centrality(FF_bin): print "NOTICE: in_degree_centrality is BINARY"
if nx.out_degree_centrality(FF) == nx.out_degree_centrality(FF_bin): print "NOTICE: out_degree_centrality is BINARY"
if nx.closeness_centrality(FF) == nx.closeness_centrality(FF_bin): print "NOTICE: closeness_centrality is BINARY"
# Eigenvector Centrality makes problems upon converging
#if nx.eigenvector_centrality(FF) == nx.eigenvector_centrality(FF_bin): print "NOTICE: eigenvector_centrality is BINARY"
if hp.individual_reciprocity(FF) == hp.individual_reciprocity(FF_bin): print "NOTICE: individual_reciprocity is BINARY"
if hp.individual_average_tie_strength(FF) == hp.individual_average_tie_strength(FF_bin): print "NOTICE: individual_average_tie_strength is BINARY"
# The in_out volumes are computed in regard to a given group D
